Main network model diagnostics - unbalanced statistics

This file shows diagnostics for main network models fit using unbalanced racial/ethnic mixing matrices and degree terms as reported by egos. In this file, we fit a series of nested models by adding one term at a time to examine changes to model estimates, MCMC diagnostics, and network diagnostics.

Load packages and model fits

rm(list = ls())
suppressMessages(library("EpiModelHIV"))
library("latticeExtra")
## Loading required package: lattice
## Loading required package: RColorBrewer
library("knitr")
library("kableExtra")

load(file = "/homes/dpwhite/R/GitHub Repos/WHAMP/Model fits and simulations/Fit tests and debugging/est/fit.m.buildup.unbal.rda")

Model terms and control settings

Model terms and target statistics
Terms Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8
edges 2240.5 2240.5 2240.5 2240.5 2240.5 2240.5 2240.5 2240.5
nodefactor.deg.pers.1 NA NA NA 474.0 474.0 474.0 474.0 474.0
nodefactor.deg.pers.2 NA NA NA 605.0 605.0 605.0 605.0 605.0
nodefactor.race..wa.B NA 208.0 208.0 208.0 208.0 208.0 208.0 208.0
nodefactor.race..wa.H NA 535.0 535.0 535.0 535.0 535.0 535.0 535.0
nodefactor.region.EW NA NA NA NA 445.6 445.6 445.6 445.6
nodefactor.region.OW NA NA NA NA 1278.1 1278.1 1278.1 1278.1
nodematch.race..wa.B NA NA 31.2 31.2 31.2 31.2 31.2 31.2
nodematch.race..wa.H NA NA 123.3 123.3 123.3 123.3 123.3 123.3
nodematch.race..wa.O NA NA 1638.9 1638.9 1638.9 1638.9 1638.9 1638.9
absdiff.sqrt.age NA NA NA NA NA 1206.3 1206.3 1206.3
degrange 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
nodematch.role.class.I -Inf -Inf -Inf -Inf -Inf -Inf -Inf -Inf
nodematch.role.class.R -Inf -Inf -Inf -Inf -Inf -Inf -Inf -Inf
mix.region.EW.KC NA NA NA NA NA NA -Inf NA
mix.region.EW.OW NA NA NA NA NA NA -Inf NA
mix.region.KC.OW NA NA NA NA NA NA -Inf NA
nodematch.region NA NA NA NA NA NA NA 2016.5

The control settings for these models are:

set.control.ergm = control.ergm(MCMC.interval = 1e+5,
                                MCMC.samplesize = 7500,
                                MCMC.burnin = 1e+6,
                                MPLE.max.dyad.types = 1e+7,
                                init.method = "zeros",
                                MCMLE.maxit = 400,
                                parallel = np/2, 
                                parallel.type="PSOCK"))

MCMC diagnostics

Model 1

## Sample statistics summary:
## 
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05 
## Number of chains = 8 
## Sample size per chain = 3750 
## 
## 1. Empirical mean and standard deviation for each variable,
##    plus standard error of the mean:
## 
##           Mean             SD       Naive SE Time-series SE 
##         0.2584        29.1738         0.1684         0.1687 
## 
## 2. Quantiles for each variable:
## 
##  2.5%   25%   50%   75% 97.5% 
## -57.5 -19.5   0.5  19.5  57.5 
## 
## 
## Sample statistics cross-correlations:
##       edges
## edges     1
## 
## Sample statistics auto-correlation:
## Chain 1 
##                  edges
## Lag 0      1.000000000
## Lag 1e+05 -0.021311759
## Lag 2e+05  0.006913747
## Lag 3e+05 -0.015821787
## Lag 4e+05  0.003054700
## Lag 5e+05  0.001659161
## Chain 2 
##                  edges
## Lag 0      1.000000000
## Lag 1e+05  0.038893096
## Lag 2e+05  0.008780638
## Lag 3e+05 -0.002287174
## Lag 4e+05 -0.020430043
## Lag 5e+05 -0.015529059
## Chain 3 
##                   edges
## Lag 0      1.0000000000
## Lag 1e+05 -0.0177714670
## Lag 2e+05 -0.0003294755
## Lag 3e+05 -0.0266891793
## Lag 4e+05  0.0088755247
## Lag 5e+05  0.0096310578
## Chain 4 
##                  edges
## Lag 0      1.000000000
## Lag 1e+05  0.012800823
## Lag 2e+05  0.007450237
## Lag 3e+05 -0.035238088
## Lag 4e+05  0.002876751
## Lag 5e+05  0.015557350
## Chain 5 
##                   edges
## Lag 0      1.0000000000
## Lag 1e+05 -0.0054548704
## Lag 2e+05 -0.0009120409
## Lag 3e+05  0.0006025041
## Lag 4e+05 -0.0079666790
## Lag 5e+05  0.0025264678
## Chain 6 
##                  edges
## Lag 0      1.000000000
## Lag 1e+05 -0.027293751
## Lag 2e+05  0.003653601
## Lag 3e+05 -0.006770046
## Lag 4e+05 -0.003018379
## Lag 5e+05  0.007868121
## Chain 7 
##                  edges
## Lag 0      1.000000000
## Lag 1e+05  0.015172629
## Lag 2e+05 -0.002182382
## Lag 3e+05 -0.006919308
## Lag 4e+05  0.006071705
## Lag 5e+05  0.009893214
## Chain 8 
##                  edges
## Lag 0      1.000000000
## Lag 1e+05 -0.001121886
## Lag 2e+05 -0.010930717
## Lag 3e+05  0.002647498
## Lag 4e+05 -0.028508689
## Lag 5e+05  0.011023553
## 
## Sample statistics burn-in diagnostic (Geweke):
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##  edges 
## -1.774 
## 
## Individual P-values (lower = worse):
##     edges 
## 0.0761076 
## Joint P-value (lower = worse):  0.110726 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
## edges 
## 1.463 
## 
## Individual P-values (lower = worse):
##   edges 
## 0.14347 
## Joint P-value (lower = worse):  0.1416173 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##  edges 
## 0.6308 
## 
## Individual P-values (lower = worse):
##    edges 
## 0.528151 
## Joint P-value (lower = worse):  0.5287019 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##   edges 
## -0.6044 
## 
## Individual P-values (lower = worse):
##    edges 
## 0.545568 
## Joint P-value (lower = worse):  0.5381292 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##   edges 
## -0.6822 
## 
## Individual P-values (lower = worse):
##     edges 
## 0.4951103 
## Joint P-value (lower = worse):  0.5236003 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##  edges 
## 0.5754 
## 
## Individual P-values (lower = worse):
##     edges 
## 0.5650061 
## Joint P-value (lower = worse):  0.5345761 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
## edges 
## 0.768 
## 
## Individual P-values (lower = worse):
##     edges 
## 0.4425155 
## Joint P-value (lower = worse):  0.4456171 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##   edges 
## -0.4798 
## 
## Individual P-values (lower = worse):
##     edges 
## 0.6313917 
## Joint P-value (lower = worse):  0.6776458 .
## Warning in formals(fun): argument is not a function

## 
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).

Model 2

## Sample statistics summary:
## 
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05 
## Number of chains = 8 
## Sample size per chain = 3750 
## 
## 1. Empirical mean and standard deviation for each variable,
##    plus standard error of the mean:
## 
##                          Mean    SD Naive SE Time-series SE
## edges                 -0.5446 29.09  0.16795        0.16519
## nodefactor.race..wa.B  0.1272 11.79  0.06808        0.06726
## nodefactor.race..wa.H  0.1142 16.79  0.09697        0.09625
## 
## 2. Quantiles for each variable:
## 
##                         2.5%     25%     50%    75% 97.5%
## edges                 -57.50 -20.500 -0.5000 19.500 56.50
## nodefactor.race..wa.B -23.00  -7.997  0.0032  8.003 23.00
## nodefactor.race..wa.H -32.98 -10.978  0.0220 11.022 33.02
## 
## 
## Sample statistics cross-correlations:
##                           edges nodefactor.race..wa.B
## edges                 1.0000000            0.27258005
## nodefactor.race..wa.B 0.2725801            1.00000000
## nodefactor.race..wa.H 0.3802482            0.02750431
##                       nodefactor.race..wa.H
## edges                            0.38024824
## nodefactor.race..wa.B            0.02750431
## nodefactor.race..wa.H            1.00000000
## 
## Sample statistics auto-correlation:
## Chain 1 
##                   edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0      1.0000000000           1.000000000          1.0000000000
## Lag 1e+05 -0.0306955142          -0.045185354          0.0003787597
## Lag 2e+05 -0.0176717306           0.003181592          0.0008145705
## Lag 3e+05 -0.0059129372          -0.033190726          0.0149143693
## Lag 4e+05 -0.0185361432           0.012874995         -0.0005899606
## Lag 5e+05 -0.0006284204          -0.037491288         -0.0036692153
## Chain 2 
##                  edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0      1.000000000           1.000000000          1.0000000000
## Lag 1e+05 -0.004735900           0.001718756         -0.0105949701
## Lag 2e+05 -0.016561893           0.003234964         -0.0071797685
## Lag 3e+05  0.021881418           0.007244600          0.0026880875
## Lag 4e+05 -0.005663435          -0.003831923          0.0001270214
## Lag 5e+05  0.029824205           0.006738128         -0.0105913886
## Chain 3 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0      1.00000000           1.000000000           1.000000000
## Lag 1e+05 -0.01058281          -0.017344375           0.020142400
## Lag 2e+05  0.01934298          -0.021331277           0.002824005
## Lag 3e+05 -0.03188353          -0.012253084           0.013549969
## Lag 4e+05  0.02926160           0.003937166          -0.004719217
## Lag 5e+05 -0.02612521           0.008052129           0.001919083
## Chain 4 
##                  edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0      1.000000000           1.000000000           1.000000000
## Lag 1e+05  0.008041777          -0.004149025          -0.021863917
## Lag 2e+05 -0.029401783          -0.025531110          -0.021383696
## Lag 3e+05  0.025536754           0.001237031           0.011432079
## Lag 4e+05  0.013367916           0.021066169          -0.019794832
## Lag 5e+05 -0.004986184          -0.005333816          -0.004319172
## Chain 5 
##                  edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0      1.000000000           1.000000000          1.0000000000
## Lag 1e+05 -0.032412281          -0.014228743         -0.0295305625
## Lag 2e+05  0.016618921          -0.013868108         -0.0275281512
## Lag 3e+05  0.010090519          -0.002592426          0.0102127976
## Lag 4e+05 -0.042199905          -0.004605539         -0.0002331337
## Lag 5e+05  0.007116046           0.005642546         -0.0178356773
## Chain 6 
##                  edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0      1.000000000           1.000000000           1.000000000
## Lag 1e+05 -0.021080979          -0.016354272          -0.006400649
## Lag 2e+05 -0.013821603           0.011133737          -0.007072587
## Lag 3e+05 -0.009287061           0.027393191          -0.004836509
## Lag 4e+05  0.037394149           0.001649983          -0.002463360
## Lag 5e+05 -0.022328145           0.024217209          -0.029483405
## Chain 7 
##                  edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0      1.000000000           1.000000000          1.0000000000
## Lag 1e+05 -0.016006268          -0.022793687          0.0030456370
## Lag 2e+05  0.003493309          -0.004603620          0.0082137286
## Lag 3e+05  0.023780886          -0.016736899         -0.0062477606
## Lag 4e+05  0.008830207          -0.008336089          0.0007973993
## Lag 5e+05 -0.010635984          -0.040810940         -0.0288721541
## Chain 8 
##                  edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0      1.000000000           1.000000000           1.000000000
## Lag 1e+05  0.016623295           0.003165565          -0.032525364
## Lag 2e+05  0.005450470           0.012551951           0.028939148
## Lag 3e+05 -0.007705185          -0.003852331           0.007713364
## Lag 4e+05  0.015687234          -0.006296764          -0.014089011
## Lag 5e+05  0.037315564          -0.015040474           0.004781505
## 
## Sample statistics burn-in diagnostic (Geweke):
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##               -1.2095               -0.8668               -0.4193 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##             0.2264760             0.3860679             0.6749928 
## Joint P-value (lower = worse):  0.6040104 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##                0.6987               -1.0471                0.0321 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##             0.4847340             0.2950420             0.9743886 
## Joint P-value (lower = worse):  0.4848073 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##                0.2831               -0.1072                0.7440 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##             0.7771285             0.9146513             0.4568527 
## Joint P-value (lower = worse):  0.9154585 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##                1.0468                0.1007                1.9788 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##            0.29520220            0.91978755            0.04783802 
## Joint P-value (lower = worse):  0.2479544 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##               2.53054              -0.05994               0.62401 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##            0.01138869            0.95220164            0.53262340 
## Joint P-value (lower = worse):  0.1302313 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##               -0.4098               -1.8064                0.2498 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##            0.68198394            0.07085786            0.80271947 
## Joint P-value (lower = worse):  0.3505584 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##                0.7146                0.4222               -1.1114 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##             0.4748388             0.6728580             0.2664013 
## Joint P-value (lower = worse):  0.3675842 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##               -0.8075               -0.8192                0.8459 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##             0.4193924             0.4126684             0.3975982 
## Joint P-value (lower = worse):  0.5046923 .
## Warning in formals(fun): argument is not a function

## 
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).

Model 3

## Sample statistics summary:
## 
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05 
## Number of chains = 8 
## Sample size per chain = 3750 
## 
## 1. Empirical mean and standard deviation for each variable,
##    plus standard error of the mean:
## 
##                         Mean     SD Naive SE Time-series SE
## edges                 -2.055 29.278  0.16904        0.17276
## nodefactor.race..wa.B  2.570 12.619  0.07286        0.07328
## nodefactor.race..wa.H  1.602 18.038  0.10414        0.11251
## nodematch.race..wa.B  -2.093  4.907  0.02833        0.02962
## nodematch.race..wa.H  -2.525  8.783  0.05071        0.06132
## nodematch.race..wa.O   2.282 26.643  0.15382        0.15383
## 
## 2. Quantiles for each variable:
## 
##                         2.5%     25%    50%     75%  97.5%
## edges                 -59.50 -21.500 -2.500 17.5000 55.500
## nodefactor.race..wa.B -22.00  -5.997  2.003 11.0032 28.003
## nodefactor.race..wa.H -33.98 -10.978  2.022 14.0220 37.022
## nodematch.race..wa.B  -11.18  -5.179 -2.179  0.8213  7.821
## nodematch.race..wa.H  -19.31  -8.312 -2.312  3.6876 14.688
## nodematch.race..wa.O  -49.89 -15.890  2.110 20.1103 55.110
## 
## 
## Sample statistics cross-correlations:
##                           edges nodefactor.race..wa.B
## edges                 1.0000000          0.2647565608
## nodefactor.race..wa.B 0.2647566          1.0000000000
## nodefactor.race..wa.H 0.3537234         -0.0008631546
## nodematch.race..wa.B  0.1056490          0.5697787625
## nodematch.race..wa.H  0.1578961          0.0011058680
## nodematch.race..wa.O  0.8055371         -0.0768092727
##                       nodefactor.race..wa.H nodematch.race..wa.B
## edges                          0.3537233612          0.105649021
## nodefactor.race..wa.B         -0.0008631546          0.569778763
## nodefactor.race..wa.H          1.0000000000          0.002158381
## nodematch.race..wa.B           0.0021583813          1.000000000
## nodematch.race..wa.H           0.6423118498         -0.004620620
## nodematch.race..wa.O          -0.0757747755          0.027413705
##                       nodematch.race..wa.H nodematch.race..wa.O
## edges                          0.157896145           0.80553714
## nodefactor.race..wa.B          0.001105868          -0.07680927
## nodefactor.race..wa.H          0.642311850          -0.07577478
## nodematch.race..wa.B          -0.004620620           0.02741370
## nodematch.race..wa.H           1.000000000           0.06691933
## nodematch.race..wa.O           0.066919332           1.00000000
## 
## Sample statistics auto-correlation:
## Chain 1 
##                  edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0      1.000000000           1.000000000           1.000000000
## Lag 1e+05  0.016519386           0.017304224           0.063025775
## Lag 2e+05 -0.004136813           0.006852805           0.028877959
## Lag 3e+05 -0.006910188          -0.014316147           0.003086294
## Lag 4e+05  0.024185191          -0.012404451           0.013000294
## Lag 5e+05 -0.001100995          -0.013426432          -0.021914577
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0               1.00000000          1.000000000          1.000000000
## Lag 1e+05           0.05372021          0.202676549          0.010609396
## Lag 2e+05           0.02039436          0.044841247          0.005967625
## Lag 3e+05          -0.03397172         -0.009542895         -0.005524379
## Lag 4e+05           0.01847732          0.003741272          0.013000612
## Lag 5e+05           0.01062949          0.015806015          0.023326138
## Chain 2 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0      1.00000000          1.0000000000           1.000000000
## Lag 1e+05  0.03170898          0.0220538974           0.042247941
## Lag 2e+05 -0.01242839          0.0073330014           0.006177381
## Lag 3e+05  0.02220543          0.0172176533           0.024663497
## Lag 4e+05  0.01848319         -0.0005187285           0.022241583
## Lag 5e+05 -0.01827191          0.0296952709          -0.024114778
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0              1.000000000           1.00000000           1.00000000
## Lag 1e+05          0.045350669           0.18081058           0.01514566
## Lag 2e+05          0.004847473           0.02671978          -0.01544780
## Lag 3e+05         -0.019326394           0.01937799           0.01314423
## Lag 4e+05         -0.016419363           0.01268967           0.02710092
## Lag 5e+05          0.008108552          -0.02253251          -0.02062901
## Chain 3 
##                   edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0      1.0000000000          1.0000000000           1.000000000
## Lag 1e+05  0.0093765692          0.0239162984           0.070469479
## Lag 2e+05  0.0334365706         -0.0424366592          -0.004885092
## Lag 3e+05  0.0284832287         -0.0104428208           0.030727177
## Lag 4e+05  0.0006965439         -0.0092699727          -0.001394110
## Lag 5e+05 -0.0318239237          0.0008410848           0.004204452
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0               1.00000000          1.000000000          1.000000000
## Lag 1e+05           0.04712306          0.175831092          0.008090358
## Lag 2e+05           0.01680676         -0.008034971          0.016527894
## Lag 3e+05          -0.02366084          0.028664430          0.014421543
## Lag 4e+05           0.01499158          0.008492331          0.005638062
## Lag 5e+05           0.01985874         -0.018280297         -0.029428361
## Chain 4 
##                  edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0      1.000000000           1.000000000           1.000000000
## Lag 1e+05  0.005779165          -0.010551662           0.075782084
## Lag 2e+05 -0.003340064           0.005274312           0.006408043
## Lag 3e+05  0.014690284          -0.011892595           0.009221204
## Lag 4e+05 -0.004360978          -0.011412664           0.013878039
## Lag 5e+05 -0.011315547           0.010612762          -0.026012898
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0              1.000000000           1.00000000          1.000000000
## Lag 1e+05          0.048291142           0.21533093          0.007813368
## Lag 2e+05         -0.002420700           0.05387946          0.002956265
## Lag 3e+05         -0.012607722           0.02232768          0.002105551
## Lag 4e+05          0.018240396           0.01297915          0.001855877
## Lag 5e+05         -0.008470551          -0.00127063         -0.024432579
## Chain 5 
##                   edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0      1.0000000000            1.00000000          1.0000000000
## Lag 1e+05  0.0081198179            0.03475842          0.0590434404
## Lag 2e+05 -0.0110171355            0.01091370         -0.0015956971
## Lag 3e+05 -0.0021593022           -0.01593856          0.0082997100
## Lag 4e+05  0.0001253672           -0.01995427          0.0053330757
## Lag 5e+05 -0.0153044350           -0.01296706          0.0007583651
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0              1.000000000          1.000000000          1.000000000
## Lag 1e+05          0.040993844          0.164998869          0.009889971
## Lag 2e+05          0.004827850          0.013497689          0.002420316
## Lag 3e+05         -0.003305413         -0.003800739          0.010790474
## Lag 4e+05         -0.010429687          0.018743993          0.009938880
## Lag 5e+05          0.008292257          0.014301661         -0.007632175
## Chain 6 
##                   edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0      1.0000000000           1.000000000           1.000000000
## Lag 1e+05  0.0175959108           0.026596425           0.040812437
## Lag 2e+05  0.0005891317           0.008209009           0.015502075
## Lag 3e+05  0.0055824580          -0.013535659           0.032977470
## Lag 4e+05 -0.0085849731          -0.029434353           0.053672469
## Lag 5e+05 -0.0160597982          -0.018208719           0.006579775
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0              1.000000000          1.000000000         1.0000000000
## Lag 1e+05          0.060941203          0.192550191         0.0104338453
## Lag 2e+05         -0.005167953          0.024864067        -0.0005054795
## Lag 3e+05          0.007097300          0.011937740         0.0238448993
## Lag 4e+05         -0.023324822          0.022254873        -0.0157308982
## Lag 5e+05         -0.016148574          0.006550317        -0.0183572028
## Chain 7 
##                   edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0      1.0000000000           1.000000000           1.000000000
## Lag 1e+05 -0.0306666136          -0.002572472           0.075778210
## Lag 2e+05 -0.0035071762           0.019391538           0.040134636
## Lag 3e+05  0.0001315622           0.009686850          -0.004056141
## Lag 4e+05 -0.0253067829          -0.024663165          -0.002224635
## Lag 5e+05 -0.0099361363           0.017542824           0.001066214
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0              1.000000000          1.000000000           1.00000000
## Lag 1e+05          0.030944645          0.180244300          -0.00768810
## Lag 2e+05          0.043555486          0.053516561           0.01028223
## Lag 3e+05          0.011514849         -0.022485054          -0.00168595
## Lag 4e+05          0.009365457         -0.015999157          -0.03495420
## Lag 5e+05         -0.014477900          0.006433953          -0.02647767
## Chain 8 
##                  edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0      1.000000000           1.000000000           1.000000000
## Lag 1e+05  0.007608538          -0.013515705           0.060294210
## Lag 2e+05 -0.008574406          -0.014774737           0.023114665
## Lag 3e+05 -0.022133905           0.012407256          -0.006987887
## Lag 4e+05 -0.005534045           0.021201057           0.009182523
## Lag 5e+05  0.008737817           0.003476892          -0.013135535
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0              1.000000000          1.000000000          1.000000000
## Lag 1e+05          0.014784872          0.201416996          0.005211081
## Lag 2e+05          0.022305515          0.031037942         -0.006495462
## Lag 3e+05          0.015088321         -0.003656476         -0.019681934
## Lag 4e+05          0.023702519          0.001526153          0.006654373
## Lag 5e+05         -0.003175929          0.015592441          0.001468278
## 
## Sample statistics burn-in diagnostic (Geweke):
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##                0.2007                1.3501               -0.2650 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##                1.0127               -0.9465               -0.4797 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##             0.8409182             0.1769982             0.7909989 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##             0.3111912             0.3438809             0.6314456 
## Joint P-value (lower = worse):  0.6906256 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##                1.5005                0.2115               -0.3640 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##                0.3874                0.3293                2.0011 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##            0.13349632            0.83247545            0.71587472 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##            0.69847620            0.74195696            0.04538503 
## Joint P-value (lower = worse):  0.6663923 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##                0.4341                1.2091               -0.6912 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##                1.0616               -1.6591                0.2075 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##            0.66425086            0.22660629            0.48941343 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##            0.28841159            0.09710331            0.83560462 
## Joint P-value (lower = worse):  0.6852257 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##               -0.4306               -0.2211                0.3419 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##               -0.6349               -0.3137               -0.8138 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##             0.6667486             0.8250033             0.7324084 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##             0.5255087             0.7537536             0.4157744 
## Joint P-value (lower = worse):  0.9569717 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##               -1.2616               -0.7943                1.7773 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##               -0.8380                0.8732               -1.9509 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##            0.20710786            0.42704384            0.07552511 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##            0.40204739            0.38257448            0.05106607 
## Joint P-value (lower = worse):  0.2838306 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##                1.2802               -0.2859               -1.3265 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##               -1.2323               -0.6576                1.9009 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##            0.20049169            0.77496789            0.18466895 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##            0.21783682            0.51081467            0.05731461 
## Joint P-value (lower = worse):  0.3574145 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##               -1.4178               -1.6539               -2.9350 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##                0.1030               -1.9992                0.3531 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##           0.156240614           0.098144209           0.003336004 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##           0.917982546           0.045581922           0.724014814 
## Joint P-value (lower = worse):  0.07356876 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##               0.94485              -0.06226               0.09763 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##              -1.02337              -0.02246               0.78247 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##             0.3447380             0.9503595             0.9222285 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##             0.3061328             0.9820830             0.4339404 
## Joint P-value (lower = worse):  0.902809 .
## Warning in formals(fun): argument is not a function

## 
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).

Model 4

## Sample statistics summary:
## 
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05 
## Number of chains = 8 
## Sample size per chain = 3750 
## 
## 1. Empirical mean and standard deviation for each variable,
##    plus standard error of the mean:
## 
##                           Mean     SD Naive SE Time-series SE
## edges                 -1.64923 28.930  0.16703        0.16646
## nodefactor.deg.pers.1  0.15100 17.404  0.10048        0.10148
## nodefactor.deg.pers.2 -0.03153 18.962  0.10947        0.10947
## nodefactor.race..wa.B  2.17023 12.600  0.07275        0.07407
## nodefactor.race..wa.H  2.43347 17.853  0.10307        0.10830
## nodematch.race..wa.B  -2.16982  4.882  0.02819        0.03045
## nodematch.race..wa.H  -2.17980  8.783  0.05071        0.06055
## nodematch.race..wa.O   2.52408 26.336  0.15205        0.15215
## 
## 2. Quantiles for each variable:
## 
##                         2.5%     25%    50%     75%  97.5%
## edges                 -58.50 -21.500 -1.500 17.5000 56.500
## nodefactor.deg.pers.1 -34.00 -12.000  0.000 12.0000 34.000
## nodefactor.deg.pers.2 -37.00 -13.000  0.000 13.0000 38.000
## nodefactor.race..wa.B -22.00  -5.997  2.003 11.0032 27.003
## nodefactor.race..wa.H -32.98  -9.978  2.022 14.0220 38.022
## nodematch.race..wa.B  -11.18  -5.179 -2.179  0.8213  7.821
## nodematch.race..wa.H  -19.31  -8.312 -2.312  3.6876 15.688
## nodematch.race..wa.O  -48.89 -14.890  2.110 20.1103 54.110
## 
## 
## Sample statistics cross-correlations:
##                           edges nodefactor.deg.pers.1
## edges                 1.0000000            0.39296376
## nodefactor.deg.pers.1 0.3929638            1.00000000
## nodefactor.deg.pers.2 0.4283944            0.05461637
## nodefactor.race..wa.B 0.2717870            0.08691547
## nodefactor.race..wa.H 0.3518713            0.15046209
## nodematch.race..wa.B  0.1087730            0.03388839
## nodematch.race..wa.H  0.1628196            0.08427869
## nodematch.race..wa.O  0.8044073            0.32248149
##                       nodefactor.deg.pers.2 nodefactor.race..wa.B
## edges                            0.42839445           0.271787036
## nodefactor.deg.pers.1            0.05461637           0.086915473
## nodefactor.deg.pers.2            1.00000000           0.103750036
## nodefactor.race..wa.B            0.10375004           1.000000000
## nodefactor.race..wa.H            0.13956435          -0.005741791
## nodematch.race..wa.B             0.03703048           0.562516465
## nodematch.race..wa.H             0.05654604          -0.006523265
## nodematch.race..wa.O             0.35206939          -0.073875076
##                       nodefactor.race..wa.H nodematch.race..wa.B
## edges                          3.518713e-01         1.087730e-01
## nodefactor.deg.pers.1          1.504621e-01         3.388839e-02
## nodefactor.deg.pers.2          1.395644e-01         3.703048e-02
## nodefactor.race..wa.B         -5.741791e-03         5.625165e-01
## nodefactor.race..wa.H          1.000000e+00         9.987202e-05
## nodematch.race..wa.B           9.987202e-05         1.000000e+00
## nodematch.race..wa.H           6.463113e-01        -7.430724e-03
## nodematch.race..wa.O          -7.305191e-02         3.319868e-02
##                       nodematch.race..wa.H nodematch.race..wa.O
## edges                          0.162819621           0.80440732
## nodefactor.deg.pers.1          0.084278693           0.32248149
## nodefactor.deg.pers.2          0.056546036           0.35206939
## nodefactor.race..wa.B         -0.006523265          -0.07387508
## nodefactor.race..wa.H          0.646311317          -0.07305191
## nodematch.race..wa.B          -0.007430724           0.03319868
## nodematch.race..wa.H           1.000000000           0.07596680
## nodematch.race..wa.O           0.075966804           1.00000000
## 
## Sample statistics auto-correlation:
## Chain 1 
##                  edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0      1.000000000           1.000000000          1.0000000000
## Lag 1e+05  0.008752603           0.019228030          0.0006564403
## Lag 2e+05 -0.019271518          -0.023798195          0.0170813001
## Lag 3e+05 -0.006083547           0.019998568         -0.0016990287
## Lag 4e+05  0.007772439          -0.012321003          0.0105806790
## Lag 5e+05 -0.036990300          -0.001257184         -0.0059260861
##           nodefactor.race..wa.B nodefactor.race..wa.H nodematch.race..wa.B
## Lag 0               1.000000000            1.00000000          1.000000000
## Lag 1e+05          -0.001671103            0.04673924          0.025219897
## Lag 2e+05          -0.002154491           -0.01197586          0.006103509
## Lag 3e+05           0.004660132            0.03860628          0.036839479
## Lag 4e+05           0.021937532            0.02010942          0.047978270
## Lag 5e+05          -0.021556767            0.01218959         -0.003853325
##           nodematch.race..wa.H nodematch.race..wa.O
## Lag 0              1.000000000          1.000000000
## Lag 1e+05          0.190668059          0.027479581
## Lag 2e+05          0.025799713         -0.019159629
## Lag 3e+05         -0.003472251         -0.030656618
## Lag 4e+05         -0.016311219         -0.004171642
## Lag 5e+05         -0.006070304         -0.013434941
## Chain 2 
##                  edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0      1.000000000          1.0000000000          1.0000000000
## Lag 1e+05 -0.010697758         -0.0001182347         -0.0190597451
## Lag 2e+05 -0.005344925         -0.0070188385          0.0042361600
## Lag 3e+05  0.041323703          0.0110430646         -0.0001722969
## Lag 4e+05 -0.010524758          0.0158358889         -0.0132680951
## Lag 5e+05  0.009058742          0.0037857708          0.0024998555
##           nodefactor.race..wa.B nodefactor.race..wa.H nodematch.race..wa.B
## Lag 0               1.000000000           1.000000000          1.000000000
## Lag 1e+05           0.037369540           0.053120908          0.056002346
## Lag 2e+05           0.006843422          -0.015382102          0.001157155
## Lag 3e+05           0.020262985          -0.023842251         -0.009688572
## Lag 4e+05          -0.017868941           0.014472006         -0.012100356
## Lag 5e+05           0.015023677          -0.007792651         -0.008210095
##           nodematch.race..wa.H nodematch.race..wa.O
## Lag 0              1.000000000           1.00000000
## Lag 1e+05          0.175695122          -0.01284143
## Lag 2e+05          0.006999300          -0.01414330
## Lag 3e+05          0.009109225           0.04393157
## Lag 4e+05          0.001466106          -0.01242009
## Lag 5e+05         -0.021482677           0.01687529
## Chain 3 
##                  edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0      1.000000000           1.000000000           1.000000000
## Lag 1e+05  0.005746642          -0.012702490           0.001768126
## Lag 2e+05 -0.019932684          -0.004405447           0.003480485
## Lag 3e+05 -0.015371414          -0.006551598          -0.012125291
## Lag 4e+05  0.005280698           0.009456580          -0.035881670
## Lag 5e+05 -0.023488759           0.006772772          -0.016418177
##           nodefactor.race..wa.B nodefactor.race..wa.H nodematch.race..wa.B
## Lag 0               1.000000000           1.000000000         1.0000000000
## Lag 1e+05          -0.008271688           0.067931804         0.0549993076
## Lag 2e+05          -0.022770391           0.001554890         0.0136730030
## Lag 3e+05           0.018125571          -0.004813337         0.0006656391
## Lag 4e+05          -0.011872193           0.021086662        -0.0002850301
## Lag 5e+05          -0.010658084           0.016924486         0.0136633996
##           nodematch.race..wa.H nodematch.race..wa.O
## Lag 0              1.000000000          1.000000000
## Lag 1e+05          0.182023395          0.010250578
## Lag 2e+05          0.033682705         -0.013749605
## Lag 3e+05          0.007563420         -0.014074040
## Lag 4e+05          0.009884538         -0.003169226
## Lag 5e+05         -0.013710888         -0.025429039
## Chain 4 
##                  edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0      1.000000000           1.000000000          1.0000000000
## Lag 1e+05 -0.012371530          -0.005058543          0.0020863377
## Lag 2e+05  0.030253006          -0.030174096          0.0004201859
## Lag 3e+05 -0.023750487           0.004536323          0.0097089427
## Lag 4e+05 -0.020740279           0.005583606         -0.0059485661
## Lag 5e+05 -0.001867668          -0.013945195         -0.0105491899
##           nodefactor.race..wa.B nodefactor.race..wa.H nodematch.race..wa.B
## Lag 0               1.000000000            1.00000000          1.000000000
## Lag 1e+05           0.029588310            0.06420881          0.082514375
## Lag 2e+05          -0.008648046            0.01773410          0.006040380
## Lag 3e+05          -0.023061402           -0.01056061          0.002003169
## Lag 4e+05          -0.003894681           -0.02919819         -0.028227305
## Lag 5e+05           0.008621708           -0.02743845          0.001207147
##           nodematch.race..wa.H nodematch.race..wa.O
## Lag 0              1.000000000          1.000000000
## Lag 1e+05          0.181251234          0.008820829
## Lag 2e+05          0.063312277          0.030487312
## Lag 3e+05          0.009355647         -0.030390897
## Lag 4e+05         -0.024663789         -0.008071894
## Lag 5e+05         -0.032638001          0.010697883
## Chain 5 
##                  edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0      1.000000000           1.000000000           1.000000000
## Lag 1e+05  0.018898741           0.022547819           0.003889324
## Lag 2e+05  0.020274373          -0.002996416           0.002225064
## Lag 3e+05 -0.008545535          -0.006890455           0.012953124
## Lag 4e+05  0.013538550          -0.014543236           0.003377963
## Lag 5e+05 -0.016983365          -0.007886537          -0.013424982
##           nodefactor.race..wa.B nodefactor.race..wa.H nodematch.race..wa.B
## Lag 0               1.000000000           1.000000000           1.00000000
## Lag 1e+05           0.010484700           0.062890685           0.05691356
## Lag 2e+05           0.007362897           0.026887969           0.02876054
## Lag 3e+05           0.002523273          -0.039285525          -0.01335445
## Lag 4e+05          -0.010673506          -0.002807968           0.02072323
## Lag 5e+05          -0.002117223           0.009522715           0.00474244
##           nodematch.race..wa.H nodematch.race..wa.O
## Lag 0               1.00000000          1.000000000
## Lag 1e+05           0.20375649          0.008672935
## Lag 2e+05           0.02571490          0.018949735
## Lag 3e+05          -0.04814642         -0.026741655
## Lag 4e+05          -0.01896733          0.017671168
## Lag 5e+05          -0.01499627         -0.022926062
## Chain 6 
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0     1.000000000           1.000000000           1.000000000
## Lag 1e+05 0.009109257           0.010495020           0.007956074
## Lag 2e+05 0.001472701           0.040634935          -0.006350537
## Lag 3e+05 0.006201858          -0.008927817           0.001732861
## Lag 4e+05 0.001834809          -0.008998462          -0.005017495
## Lag 5e+05 0.003893267          -0.009007922          -0.009645698
##           nodefactor.race..wa.B nodefactor.race..wa.H nodematch.race..wa.B
## Lag 0              1.0000000000           1.000000000          1.000000000
## Lag 1e+05          0.0307391436           0.082889669          0.073428246
## Lag 2e+05         -0.0124878115           0.014949979          0.010192826
## Lag 3e+05          0.0001052228           0.026791792         -0.025156531
## Lag 4e+05          0.0167301897           0.009903798          0.011027823
## Lag 5e+05          0.0023612081           0.002515532         -0.002080345
##           nodematch.race..wa.H nodematch.race..wa.O
## Lag 0              1.000000000         1.0000000000
## Lag 1e+05          0.192324014        -0.0060483765
## Lag 2e+05          0.029353116        -0.0009158723
## Lag 3e+05          0.020080320         0.0055933199
## Lag 4e+05         -0.003683553        -0.0105290115
## Lag 5e+05         -0.019684373        -0.0081958620
## Chain 7 
##                  edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0      1.000000000           1.000000000           1.000000000
## Lag 1e+05  0.005096332           0.025493447          -0.007676836
## Lag 2e+05 -0.005489997           0.007289568           0.003478120
## Lag 3e+05  0.017654406          -0.023556417           0.006255663
## Lag 4e+05  0.020597661           0.006049256          -0.001102295
## Lag 5e+05  0.022602322          -0.009939594           0.023066007
##           nodefactor.race..wa.B nodefactor.race..wa.H nodematch.race..wa.B
## Lag 0               1.000000000          1.0000000000           1.00000000
## Lag 1e+05           0.031784465          0.0430468827           0.07539525
## Lag 2e+05          -0.018745677         -0.0004698454           0.03526816
## Lag 3e+05          -0.005950322         -0.0308063434           0.00816233
## Lag 4e+05           0.036381817          0.0002043636           0.01720017
## Lag 5e+05          -0.005699507          0.0157061583          -0.01228428
##           nodematch.race..wa.H nodematch.race..wa.O
## Lag 0             1.0000000000         1.0000000000
## Lag 1e+05         0.1558428180        -0.0001961639
## Lag 2e+05         0.0237604682        -0.0059886316
## Lag 3e+05        -0.0006294228         0.0259481869
## Lag 4e+05         0.0057267178         0.0214249884
## Lag 5e+05        -0.0233835344         0.0228193497
## Chain 8 
##                  edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0      1.000000000           1.000000000           1.000000000
## Lag 1e+05 -0.006869288           0.007038851           0.005831157
## Lag 2e+05 -0.011398263          -0.012982839          -0.015520681
## Lag 3e+05 -0.002598932           0.005553033          -0.029045748
## Lag 4e+05  0.019492107          -0.012750133           0.014933520
## Lag 5e+05 -0.004375590          -0.016457340           0.002095295
##           nodefactor.race..wa.B nodefactor.race..wa.H nodematch.race..wa.B
## Lag 0              1.000000e+00           1.000000000          1.000000000
## Lag 1e+05          1.099943e-02           0.052269640          0.083729970
## Lag 2e+05         -6.652445e-03           0.007123053         -0.003486957
## Lag 3e+05          2.299394e-02          -0.036017638         -0.003877512
## Lag 4e+05         -1.689457e-05          -0.009795525          0.012235793
## Lag 5e+05         -6.031556e-03          -0.016328633          0.012627583
##           nodematch.race..wa.H nodematch.race..wa.O
## Lag 0              1.000000000          1.000000000
## Lag 1e+05          0.178373512         -0.011269106
## Lag 2e+05          0.047079012          0.002388994
## Lag 3e+05         -0.013091584          0.002807671
## Lag 4e+05         -0.025336573          0.018222642
## Lag 5e+05         -0.007292732         -0.002810882
## 
## Sample statistics burn-in diagnostic (Geweke):
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2 
##               0.03568               1.51188              -0.16927 
## nodefactor.race..wa.B nodefactor.race..wa.H  nodematch.race..wa.B 
##               0.77239              -0.93972               0.03346 
##  nodematch.race..wa.H  nodematch.race..wa.O 
##              -1.19308              -0.18921 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2 
##             0.9715368             0.1305650             0.8655882 
## nodefactor.race..wa.B nodefactor.race..wa.H  nodematch.race..wa.B 
##             0.4398860             0.3473597             0.9733050 
##  nodematch.race..wa.H  nodematch.race..wa.O 
##             0.2328384             0.8499299 
## Joint P-value (lower = worse):  0.7722156 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2 
##              -0.27935              -0.06739              -0.76275 
## nodefactor.race..wa.B nodefactor.race..wa.H  nodematch.race..wa.B 
##              -0.24010               0.43825              -1.27736 
##  nodematch.race..wa.H  nodematch.race..wa.O 
##              -0.66195              -0.80142 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2 
##             0.7799762             0.9462702             0.4456123 
## nodefactor.race..wa.B nodefactor.race..wa.H  nodematch.race..wa.B 
##             0.8102508             0.6612030             0.2014766 
##  nodematch.race..wa.H  nodematch.race..wa.O 
##             0.5080057             0.4228894 
## Joint P-value (lower = worse):  0.8158355 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2 
##               1.50275               0.07477               1.83482 
## nodefactor.race..wa.B nodefactor.race..wa.H  nodematch.race..wa.B 
##               0.41527              -0.61573              -0.57168 
##  nodematch.race..wa.H  nodematch.race..wa.O 
##              -0.56053               1.53715 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2 
##            0.13290290            0.94039903            0.06653209 
## nodefactor.race..wa.B nodefactor.race..wa.H  nodematch.race..wa.B 
##            0.67794118            0.53807013            0.56753595 
##  nodematch.race..wa.H  nodematch.race..wa.O 
##            0.57512087            0.12425637 
## Joint P-value (lower = worse):  0.6407243 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2 
##               0.02398              -1.18162               1.31616 
## nodefactor.race..wa.B nodefactor.race..wa.H  nodematch.race..wa.B 
##               1.14171              -0.73987               0.94594 
##  nodematch.race..wa.H  nodematch.race..wa.O 
##              -0.61694              -0.09553 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2 
##             0.9808698             0.2373570             0.1881195 
## nodefactor.race..wa.B nodefactor.race..wa.H  nodematch.race..wa.B 
##             0.2535732             0.4593776             0.3441771 
##  nodematch.race..wa.H  nodematch.race..wa.O 
##             0.5372775             0.9238938 
## Joint P-value (lower = worse):  0.720598 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2 
##               1.95769               0.76068               0.53151 
## nodefactor.race..wa.B nodefactor.race..wa.H  nodematch.race..wa.B 
##              -0.13792               0.03540               0.02277 
##  nodematch.race..wa.H  nodematch.race..wa.O 
##              -1.46254               1.30968 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2 
##            0.05026688            0.44685043            0.59506201 
## nodefactor.race..wa.B nodefactor.race..wa.H  nodematch.race..wa.B 
##            0.89030367            0.97176205            0.98183750 
##  nodematch.race..wa.H  nodematch.race..wa.O 
##            0.14359447            0.19030487 
## Joint P-value (lower = worse):  0.412635 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2 
##               -0.3954                1.0232               -0.2737 
## nodefactor.race..wa.B nodefactor.race..wa.H  nodematch.race..wa.B 
##                0.8373               -0.7390               -0.6287 
##  nodematch.race..wa.H  nodematch.race..wa.O 
##               -0.1005               -0.4319 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2 
##             0.6925830             0.3062140             0.7843004 
## nodefactor.race..wa.B nodefactor.race..wa.H  nodematch.race..wa.B 
##             0.4024087             0.4599071             0.5295564 
##  nodematch.race..wa.H  nodematch.race..wa.O 
##             0.9199855             0.6658041 
## Joint P-value (lower = worse):  0.7073712 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2 
##                1.9267                1.2938                2.3302 
## nodefactor.race..wa.B nodefactor.race..wa.H  nodematch.race..wa.B 
##                1.1376               -0.1476               -0.6048 
##  nodematch.race..wa.H  nodematch.race..wa.O 
##                1.5008                1.9939 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2 
##            0.05401854            0.19572163            0.01979509 
## nodefactor.race..wa.B nodefactor.race..wa.H  nodematch.race..wa.B 
##            0.25529806            0.88266652            0.54531298 
##  nodematch.race..wa.H  nodematch.race..wa.O 
##            0.13340847            0.04616803 
## Joint P-value (lower = worse):  0.05175809 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2 
##               0.91068              -0.56593              -0.54818 
## nodefactor.race..wa.B nodefactor.race..wa.H  nodematch.race..wa.B 
##               0.58966              -1.83451              -0.02352 
##  nodematch.race..wa.H  nodematch.race..wa.O 
##              -2.11509               1.21750 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2 
##            0.36246212            0.57143870            0.58357081 
## nodefactor.race..wa.B nodefactor.race..wa.H  nodematch.race..wa.B 
##            0.55542106            0.06657767            0.98123730 
##  nodematch.race..wa.H  nodematch.race..wa.O 
##            0.03442239            0.22341349 
## Joint P-value (lower = worse):  0.385835 .
## Warning in formals(fun): argument is not a function

## 
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).

Model 5

## Sample statistics summary:
## 
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05 
## Number of chains = 8 
## Sample size per chain = 3750 
## 
## 1. Empirical mean and standard deviation for each variable,
##    plus standard error of the mean:
## 
##                          Mean     SD Naive SE Time-series SE
## edges                 -2.4174 28.874  0.16670        0.17064
## nodefactor.deg.pers.1  0.2471 17.360  0.10023        0.10243
## nodefactor.deg.pers.2  0.1651 18.882  0.10902        0.10793
## nodefactor.race..wa.B  2.2759 12.534  0.07236        0.07314
## nodefactor.race..wa.H  2.1733 17.718  0.10229        0.10894
## nodefactor.region.EW   0.1258 16.114  0.09303        0.09388
## nodefactor.region.OW  -0.3981 29.172  0.16842        0.16866
## nodematch.race..wa.B  -2.2905  4.875  0.02814        0.03013
## nodematch.race..wa.H  -2.0121  8.669  0.05005        0.06135
## nodematch.race..wa.O   1.9575 26.402  0.15243        0.15536
## 
## 2. Quantiles for each variable:
## 
##                         2.5%     25%     50%     75%  97.5%
## edges                 -58.50 -22.500 -2.5000 16.5000 54.500
## nodefactor.deg.pers.1 -34.00 -11.000  0.0000 12.0000 34.000
## nodefactor.deg.pers.2 -37.00 -12.000  0.0000 13.0000 37.000
## nodefactor.race..wa.B -22.00  -5.997  2.0032 11.0032 27.003
## nodefactor.race..wa.H -31.98  -9.978  2.0220 14.0220 37.022
## nodefactor.region.EW  -31.56 -10.561  0.4392 11.4392 31.439
## nodefactor.region.OW  -57.13 -20.131 -0.1306 19.1194 56.869
## nodematch.race..wa.B  -11.18  -5.179 -2.1787  0.8213  7.821
## nodematch.race..wa.H  -19.31  -7.312 -2.3124  3.6876 14.688
## nodematch.race..wa.O  -48.89 -15.890  2.1103 20.1103 54.110
## 
## 
## Sample statistics cross-correlations:
##                           edges nodefactor.deg.pers.1
## edges                 1.0000000            0.38920201
## nodefactor.deg.pers.1 0.3892020            1.00000000
## nodefactor.deg.pers.2 0.4315629            0.04625543
## nodefactor.race..wa.B 0.2608158            0.09189919
## nodefactor.race..wa.H 0.3501165            0.14821436
## nodefactor.region.EW  0.3683386            0.16693475
## nodefactor.region.OW  0.6236113            0.22454589
## nodematch.race..wa.B  0.1046966            0.03169349
## nodematch.race..wa.H  0.1594940            0.07111244
## nodematch.race..wa.O  0.8065444            0.31174861
##                       nodefactor.deg.pers.2 nodefactor.race..wa.B
## edges                            0.43156287          0.2608158221
## nodefactor.deg.pers.1            0.04625543          0.0918991881
## nodefactor.deg.pers.2            1.00000000          0.0933312678
## nodefactor.race..wa.B            0.09333127          1.0000000000
## nodefactor.race..wa.H            0.14437082         -0.0126352451
## nodefactor.region.EW             0.16008876          0.0386719268
## nodefactor.region.OW             0.25718348          0.1270393357
## nodematch.race..wa.B             0.03510175          0.5570690233
## nodematch.race..wa.H             0.06650089         -0.0007580856
## nodematch.race..wa.O             0.35908929         -0.0784044756
##                       nodefactor.race..wa.H nodefactor.region.EW
## edges                           0.350116474           0.36833860
## nodefactor.deg.pers.1           0.148214362           0.16693475
## nodefactor.deg.pers.2           0.144370823           0.16008876
## nodefactor.race..wa.B          -0.012635245           0.03867193
## nodefactor.race..wa.H           1.000000000           0.25270491
## nodefactor.region.EW            0.252704913           1.00000000
## nodefactor.region.OW            0.206448288           0.05770138
## nodematch.race..wa.B           -0.001412931           0.01199184
## nodematch.race..wa.H            0.637817567           0.13191317
## nodematch.race..wa.O           -0.073009634           0.26040657
##                       nodefactor.region.OW nodematch.race..wa.B
## edges                           0.62361129          0.104696584
## nodefactor.deg.pers.1           0.22454589          0.031693494
## nodefactor.deg.pers.2           0.25718348          0.035101747
## nodefactor.race..wa.B           0.12703934          0.557069023
## nodefactor.race..wa.H           0.20644829         -0.001412931
## nodefactor.region.EW            0.05770138          0.011991836
## nodefactor.region.OW            1.00000000          0.043664364
## nodematch.race..wa.B            0.04366436          1.000000000
## nodematch.race..wa.H            0.09053518          0.004155459
## nodematch.race..wa.O            0.52092798          0.036986142
##                       nodematch.race..wa.H nodematch.race..wa.O
## edges                         0.1594939792           0.80654441
## nodefactor.deg.pers.1         0.0711124369           0.31174861
## nodefactor.deg.pers.2         0.0665008920           0.35908929
## nodefactor.race..wa.B        -0.0007580856          -0.07840448
## nodefactor.race..wa.H         0.6378175672          -0.07300963
## nodefactor.region.EW          0.1319131662           0.26040657
## nodefactor.region.OW          0.0905351783           0.52092798
## nodematch.race..wa.B          0.0041554585           0.03698614
## nodematch.race..wa.H          1.0000000000           0.07587968
## nodematch.race..wa.O          0.0758796776           1.00000000
## 
## Sample statistics auto-correlation:
## Chain 1 
##                  edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0      1.000000000           1.000000000           1.000000000
## Lag 1e+05  0.001049806          -0.028478905          -0.005672202
## Lag 2e+05 -0.039497494          -0.011966225          -0.022325522
## Lag 3e+05  0.018347509           0.003504927          -0.005642327
## Lag 4e+05 -0.005827387           0.003954342          -0.007101611
## Lag 5e+05 -0.007702701          -0.014145071           0.021403180
##           nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0               1.000000000           1.000000000         1.0000000000
## Lag 1e+05           0.003750884           0.029700751         0.0018448784
## Lag 2e+05          -0.026698597           0.009572208        -0.0009416475
## Lag 3e+05          -0.008801070           0.024189849         0.0238007061
## Lag 4e+05           0.001239073          -0.007585066         0.0004980398
## Lag 5e+05          -0.012352484           0.004127481         0.0176534745
##           nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0               1.00000000          1.000000000           1.00000000
## Lag 1e+05           0.01310701          0.052610802           0.17990509
## Lag 2e+05          -0.02601239          0.007609806           0.04675860
## Lag 3e+05          -0.02483874         -0.009572847           0.02692782
## Lag 4e+05           0.01820572          0.006378743          -0.02596679
## Lag 5e+05          -0.03912374         -0.013125159          -0.02291148
##           nodematch.race..wa.O
## Lag 0             1.0000000000
## Lag 1e+05         0.0148688080
## Lag 2e+05        -0.0281587757
## Lag 3e+05         0.0004260314
## Lag 4e+05         0.0037552797
## Lag 5e+05         0.0019121699
## Chain 2 
##                  edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0     1.0000000000           1.000000000           1.000000000
## Lag 1e+05 0.0006277275           0.006253874           0.038817679
## Lag 2e+05 0.0081645791           0.025140475          -0.012193937
## Lag 3e+05 0.0397651132           0.019229896          -0.015194345
## Lag 4e+05 0.0283929187           0.036593033          -0.007596629
## Lag 5e+05 0.0486225609           0.030858222          -0.020535525
##           nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0               1.000000000            1.00000000          1.000000000
## Lag 1e+05           0.043955781            0.05102087          0.007626162
## Lag 2e+05          -0.016223983            0.01552412         -0.003143505
## Lag 3e+05           0.017241617           -0.01344778         -0.014030363
## Lag 4e+05           0.016678137            0.01831750         -0.001125035
## Lag 5e+05           0.006373996           -0.01296563         -0.019953405
##           nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0              1.000000000          1.000000000          1.000000000
## Lag 1e+05         -0.015915952          0.058636920          0.212948507
## Lag 2e+05         -0.010484841         -0.002589235          0.051510219
## Lag 3e+05          0.036306268          0.033242670          0.024646104
## Lag 4e+05          0.008710409         -0.007650838         -0.007886587
## Lag 5e+05          0.007750022          0.011907151         -0.009332105
##           nodematch.race..wa.O
## Lag 0             1.0000000000
## Lag 1e+05         0.0022493610
## Lag 2e+05        -0.0007049176
## Lag 3e+05         0.0367553292
## Lag 4e+05         0.0185992582
## Lag 5e+05         0.0293059516
## Chain 3 
##                  edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0      1.000000000           1.000000000           1.000000000
## Lag 1e+05  0.003741188           0.015298817          -0.009677935
## Lag 2e+05  0.046833384          -0.009200224          -0.025592757
## Lag 3e+05  0.018265805          -0.003842867           0.006622575
## Lag 4e+05 -0.008338919           0.011359737           0.019763599
## Lag 5e+05 -0.032124750          -0.034304239          -0.038806258
##           nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0               1.000000000          1.0000000000          1.000000000
## Lag 1e+05          -0.006634762          0.0676560654          0.028796986
## Lag 2e+05           0.015660030          0.0277743244          0.008402996
## Lag 3e+05          -0.010680199         -0.0002717566          0.033119585
## Lag 4e+05          -0.007197192         -0.0060549420         -0.004357217
## Lag 5e+05          -0.009538568         -0.0172293109         -0.026678894
##           nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0              1.000000000          1.000000000          1.000000000
## Lag 1e+05          0.002403779          0.033340242          0.194970239
## Lag 2e+05          0.016344411          0.041199224          0.035067617
## Lag 3e+05         -0.004064261         -0.023666068          0.031848960
## Lag 4e+05         -0.037027587          0.009525003          0.001122687
## Lag 5e+05         -0.007076284         -0.012450715         -0.018689601
##           nodematch.race..wa.O
## Lag 0              1.000000000
## Lag 1e+05          0.003132856
## Lag 2e+05         -0.002291626
## Lag 3e+05          0.001808988
## Lag 4e+05         -0.009839553
## Lag 5e+05         -0.009278227
## Chain 4 
##                  edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0      1.000000000            1.00000000           1.000000000
## Lag 1e+05  0.027738521            0.01136658           0.007472435
## Lag 2e+05 -0.002293374           -0.02656879           0.002642186
## Lag 3e+05 -0.009436103            0.01278375          -0.027526179
## Lag 4e+05  0.010044563           -0.01721805           0.028670077
## Lag 5e+05  0.009357784           -0.02321401           0.017304527
##           nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0               1.000000000           1.000000000         1.0000000000
## Lag 1e+05           0.031935591           0.066377989         0.0129912684
## Lag 2e+05          -0.011868891           0.007658114         0.0191680867
## Lag 3e+05           0.015816824           0.021566521        -0.0002266523
## Lag 4e+05          -0.007499524           0.018675762         0.0126467584
## Lag 5e+05           0.000675882          -0.010547651         0.0350177187
##           nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0              1.000000000         1.0000000000           1.00000000
## Lag 1e+05         -0.005491499         0.0866003333           0.19780660
## Lag 2e+05         -0.020422301        -0.0102266964           0.04288614
## Lag 3e+05         -0.005092385         0.0009030007           0.01441477
## Lag 4e+05          0.005015785        -0.0205809099          -0.01062330
## Lag 5e+05          0.026163667         0.0038120877          -0.01339582
##           nodematch.race..wa.O
## Lag 0              1.000000000
## Lag 1e+05          0.017249442
## Lag 2e+05         -0.002796974
## Lag 3e+05          0.014455339
## Lag 4e+05          0.012997695
## Lag 5e+05          0.015754334
## Chain 5 
##                  edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0      1.000000000           1.000000000           1.000000000
## Lag 1e+05  0.004568370           0.016948720          -0.016889539
## Lag 2e+05 -0.016679709          -0.009102403          -0.029127595
## Lag 3e+05  0.013020125          -0.008114648           0.009070979
## Lag 4e+05  0.006803005           0.013953889          -0.003579536
## Lag 5e+05 -0.016995002          -0.017422838           0.005255772
##           nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0               1.000000000            1.00000000          1.000000000
## Lag 1e+05           0.020125108            0.05887490          0.009821780
## Lag 2e+05           0.004975566            0.01065868         -0.019754080
## Lag 3e+05           0.002784859            0.01202140         -0.026648531
## Lag 4e+05          -0.013899384            0.02831271         -0.009762018
## Lag 5e+05          -0.012253916           -0.01502716         -0.009938553
##           nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0               1.00000000          1.000000000           1.00000000
## Lag 1e+05           0.01285476          0.065451551           0.19279767
## Lag 2e+05          -0.01554584          0.001496019           0.05275444
## Lag 3e+05           0.00944926         -0.014140632           0.01793667
## Lag 4e+05          -0.01282132         -0.009620996           0.02603839
## Lag 5e+05          -0.02484438          0.006903983           0.03022297
##           nodematch.race..wa.O
## Lag 0              1.000000000
## Lag 1e+05         -0.009847678
## Lag 2e+05         -0.020254346
## Lag 3e+05          0.024423972
## Lag 4e+05          0.012393142
## Lag 5e+05         -0.005749656
## Chain 6 
##                  edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0      1.000000000          1.0000000000           1.000000000
## Lag 1e+05  0.025512829          0.0185239189          -0.020617830
## Lag 2e+05  0.016762751         -0.0002942647          -0.034284650
## Lag 3e+05 -0.015219888          0.0203216091          -0.022058233
## Lag 4e+05  0.005982033          0.0251427045           0.009259874
## Lag 5e+05 -0.005076096         -0.0053059041          -0.040920351
##           nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0               1.000000000           1.000000000           1.00000000
## Lag 1e+05           0.018118508           0.075499402           0.01445953
## Lag 2e+05          -0.009069013           0.015650105           0.02595361
## Lag 3e+05          -0.001758302          -0.007933772          -0.01292903
## Lag 4e+05           0.003451277           0.004356847           0.00378161
## Lag 5e+05           0.005590283          -0.027026383          -0.01594278
##           nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0              1.000000000          1.000000000          1.000000000
## Lag 1e+05          0.030003359          0.058192754          0.188001879
## Lag 2e+05         -0.007024628         -0.008018904          0.028406665
## Lag 3e+05         -0.012885326          0.028537447          0.015382544
## Lag 4e+05          0.018883858         -0.010598214          0.001843182
## Lag 5e+05          0.007995351          0.003278633         -0.013645727
##           nodematch.race..wa.O
## Lag 0              1.000000000
## Lag 1e+05          0.027975940
## Lag 2e+05          0.026655080
## Lag 3e+05         -0.017745941
## Lag 4e+05          0.003079083
## Lag 5e+05          0.008566155
## Chain 7 
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0      1.00000000          1.000000e+00          1.0000000000
## Lag 1e+05  0.03145656          2.795297e-02          0.0236810064
## Lag 2e+05  0.01040132          9.234836e-03         -0.0002621985
## Lag 3e+05 -0.02164488          2.278715e-02         -0.0252862123
## Lag 4e+05 -0.02010451          2.893420e-03         -0.0088712187
## Lag 5e+05  0.01051864         -9.261249e-05          0.0184195682
##           nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0               1.000000000           1.000000000          1.000000000
## Lag 1e+05           0.017327785           0.068949989          0.017800784
## Lag 2e+05          -0.004155202           0.001349412         -0.002856958
## Lag 3e+05          -0.033302778          -0.029239852         -0.002857431
## Lag 4e+05           0.025646332          -0.016309873          0.009369328
## Lag 5e+05           0.009261599           0.030900937          0.034858846
##           nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0               1.00000000          1.000000000          1.000000000
## Lag 1e+05           0.02553139          0.065296956          0.197568239
## Lag 2e+05          -0.01649843          0.010843449          0.046797511
## Lag 3e+05          -0.01377140         -0.030590804          0.005528565
## Lag 4e+05          -0.01338507          0.013258773          0.020987575
## Lag 5e+05           0.01148116          0.002268468          0.018986126
##           nodematch.race..wa.O
## Lag 0              1.000000000
## Lag 1e+05          0.026811272
## Lag 2e+05         -0.008549901
## Lag 3e+05         -0.029742325
## Lag 4e+05         -0.022582580
## Lag 5e+05          0.019698304
## Chain 8 
##                  edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0      1.000000000          1.0000000000           1.000000000
## Lag 1e+05 -0.006977888          0.0007445388          -0.016761577
## Lag 2e+05 -0.007231029         -0.0055756346           0.011502687
## Lag 3e+05 -0.003276919         -0.0173546777          -0.005546904
## Lag 4e+05 -0.001947098         -0.0023433084          -0.029720977
## Lag 5e+05  0.014270561         -0.0140610904           0.003463717
##           nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0               1.000000000          1.0000000000         1.0000000000
## Lag 1e+05           0.005921685          0.0805253190         0.0002463126
## Lag 2e+05           0.003723512         -0.0056013980         0.0148900909
## Lag 3e+05           0.008547017         -0.0004304767         0.0060599927
## Lag 4e+05          -0.009639112         -0.0189095450        -0.0080202259
## Lag 5e+05           0.012296509         -0.0106714345        -0.0211186203
##           nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0             1.0000000000          1.000000000         1.0000000000
## Lag 1e+05         0.0001511049          0.079541392         0.1971588217
## Lag 2e+05        -0.0248934242          0.004784168         0.0324590539
## Lag 3e+05        -0.0149022723         -0.028291105        -0.0076538485
## Lag 4e+05         0.0068390989         -0.027676783        -0.0049169511
## Lag 5e+05        -0.0033314549         -0.009529501         0.0002696863
##           nodematch.race..wa.O
## Lag 0              1.000000000
## Lag 1e+05         -0.024454371
## Lag 2e+05         -0.005944140
## Lag 3e+05         -0.012051068
## Lag 4e+05         -0.003311608
## Lag 5e+05          0.001627818
## 
## Sample statistics burn-in diagnostic (Geweke):
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2 
##               -0.3632               -0.7893                0.1253 
## nodefactor.race..wa.B nodefactor.race..wa.H  nodefactor.region.EW 
##               -0.6660               -0.6720               -0.1384 
##  nodefactor.region.OW  nodematch.race..wa.B  nodematch.race..wa.H 
##               -0.6898               -0.4301               -0.3662 
##  nodematch.race..wa.O 
##                0.1202 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2 
##             0.7164774             0.4299522             0.9002729 
## nodefactor.race..wa.B nodefactor.race..wa.H  nodefactor.region.EW 
##             0.5054087             0.5015535             0.8899030 
##  nodefactor.region.OW  nodematch.race..wa.B  nodematch.race..wa.H 
##             0.4903285             0.6671221             0.7142074 
##  nodematch.race..wa.O 
##             0.9042866 
## Joint P-value (lower = worse):  0.9978362 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2 
##                0.9786                1.2046               -0.4787 
## nodefactor.race..wa.B nodefactor.race..wa.H  nodefactor.region.EW 
##                1.2104                1.3730               -0.3877 
##  nodefactor.region.OW  nodematch.race..wa.B  nodematch.race..wa.H 
##                0.7010                0.5647               -1.3596 
##  nodematch.race..wa.O 
##               -0.6873 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2 
##             0.3277814             0.2283682             0.6321707 
## nodefactor.race..wa.B nodefactor.race..wa.H  nodefactor.region.EW 
##             0.2261326             0.1697533             0.6982176 
##  nodefactor.region.OW  nodematch.race..wa.B  nodematch.race..wa.H 
##             0.4832810             0.5722481             0.1739581 
##  nodematch.race..wa.O 
##             0.4918915 
## Joint P-value (lower = worse):  0.1991235 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2 
##              -1.12968               0.27181              -0.44625 
## nodefactor.race..wa.B nodefactor.race..wa.H  nodefactor.region.EW 
##               0.47738               0.24686              -0.74424 
##  nodefactor.region.OW  nodematch.race..wa.B  nodematch.race..wa.H 
##               0.07371              -0.09504               0.34314 
##  nodematch.race..wa.O 
##              -1.63934 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2 
##             0.2586119             0.7857677             0.6554191 
## nodefactor.race..wa.B nodefactor.race..wa.H  nodefactor.region.EW 
##             0.6330885             0.8050152             0.4567318 
##  nodefactor.region.OW  nodematch.race..wa.B  nodematch.race..wa.H 
##             0.9412416             0.9242795             0.7314923 
##  nodematch.race..wa.O 
##             0.1011417 
## Joint P-value (lower = worse):  0.9311008 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2 
##              -0.55282              -1.45148              -0.33532 
## nodefactor.race..wa.B nodefactor.race..wa.H  nodefactor.region.EW 
##              -0.37941               0.02449              -2.17140 
##  nodefactor.region.OW  nodematch.race..wa.B  nodematch.race..wa.H 
##              -1.89456              -0.58095               0.77782 
##  nodematch.race..wa.O 
##              -0.25258 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2 
##            0.58038385            0.14664578            0.73738597 
## nodefactor.race..wa.B nodefactor.race..wa.H  nodefactor.region.EW 
##            0.70438548            0.98045799            0.02990102 
##  nodefactor.region.OW  nodematch.race..wa.B  nodematch.race..wa.H 
##            0.05815037            0.56127415            0.43667360 
##  nodematch.race..wa.O 
##            0.80058970 
## Joint P-value (lower = worse):  0.2460129 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2 
##              -0.32258               1.22109              -0.03025 
## nodefactor.race..wa.B nodefactor.race..wa.H  nodefactor.region.EW 
##              -1.33644               0.71713              -0.16281 
##  nodefactor.region.OW  nodematch.race..wa.B  nodematch.race..wa.H 
##               0.34424              -0.96992              -0.70986 
##  nodematch.race..wa.O 
##              -0.67772 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2 
##             0.7470137             0.2220526             0.9758716 
## nodefactor.race..wa.B nodefactor.race..wa.H  nodefactor.region.EW 
##             0.1814040             0.4732959             0.8706671 
##  nodefactor.region.OW  nodematch.race..wa.B  nodematch.race..wa.H 
##             0.7306687             0.3320864             0.4777906 
##  nodematch.race..wa.O 
##             0.4979524 
## Joint P-value (lower = worse):  0.7100157 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2 
##              -0.04674              -0.53744               0.23625 
## nodefactor.race..wa.B nodefactor.race..wa.H  nodefactor.region.EW 
##              -0.28257               0.39418              -0.26489 
##  nodefactor.region.OW  nodematch.race..wa.B  nodematch.race..wa.H 
##               0.05278               1.84064               0.65116 
##  nodematch.race..wa.O 
##               0.37321 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2 
##            0.96272354            0.59096360            0.81323979 
## nodefactor.race..wa.B nodefactor.race..wa.H  nodefactor.region.EW 
##            0.77750797            0.69345160            0.79109267 
##  nodefactor.region.OW  nodematch.race..wa.B  nodematch.race..wa.H 
##            0.95790499            0.06567364            0.51494441 
##  nodematch.race..wa.O 
##            0.70898993 
## Joint P-value (lower = worse):  0.7736824 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2 
##               0.68781               0.05365              -0.29850 
## nodefactor.race..wa.B nodefactor.race..wa.H  nodefactor.region.EW 
##              -0.06537               0.37912               0.80015 
##  nodefactor.region.OW  nodematch.race..wa.B  nodematch.race..wa.H 
##               0.41523               0.65461               0.24082 
##  nodematch.race..wa.O 
##               0.73678 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2 
##             0.4915751             0.9572164             0.7653245 
## nodefactor.race..wa.B nodefactor.race..wa.H  nodefactor.region.EW 
##             0.9478808             0.7045991             0.4236225 
##  nodefactor.region.OW  nodematch.race..wa.B  nodematch.race..wa.H 
##             0.6779726             0.5127212             0.8096970 
##  nodematch.race..wa.O 
##             0.4612540 
## Joint P-value (lower = worse):  0.9935162 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2 
##               -0.4718                0.3774               -1.5370 
## nodefactor.race..wa.B nodefactor.race..wa.H  nodefactor.region.EW 
##                2.1207               -2.6605                0.9492 
##  nodefactor.region.OW  nodematch.race..wa.B  nodematch.race..wa.H 
##               -0.4621                0.6931               -1.5421 
##  nodematch.race..wa.O 
##               -0.0287 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2 
##           0.637091483           0.705897736           0.124298233 
## nodefactor.race..wa.B nodefactor.race..wa.H  nodefactor.region.EW 
##           0.033951121           0.007802187           0.342527570 
##  nodefactor.region.OW  nodematch.race..wa.B  nodematch.race..wa.H 
##           0.644014125           0.488277394           0.123039375 
##  nodematch.race..wa.O 
##           0.977101373 
## Joint P-value (lower = worse):  0.04426427 .
## Warning in formals(fun): argument is not a function

## 
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).

Model 6

## Sample statistics summary:
## 
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05 
## Number of chains = 8 
## Sample size per chain = 3750 
## 
## 1. Empirical mean and standard deviation for each variable,
##    plus standard error of the mean:
## 
##                          Mean     SD Naive SE Time-series SE
## edges                 -1.7396 28.675  0.16556        0.16723
## nodefactor.deg.pers.1  0.5422 17.412  0.10053        0.10323
## nodefactor.deg.pers.2 -0.3173 18.852  0.10884        0.11061
## nodefactor.race..wa.B  2.0365 12.444  0.07185        0.07558
## nodefactor.race..wa.H  2.4968 17.599  0.10161        0.12171
## nodefactor.region.EW   0.4809 15.801  0.09123        0.09424
## nodefactor.region.OW   0.1259 29.330  0.16934        0.17307
## nodematch.race..wa.B  -2.2835  4.865  0.02809        0.03460
## nodematch.race..wa.H  -2.0655  8.699  0.05022        0.07654
## nodematch.race..wa.O   2.5048 26.407  0.15246        0.15411
## absdiff.sqrt.age       0.0810 28.536  0.16475        0.16679
## 
## 2. Quantiles for each variable:
## 
##                         2.5%     25%      50%     75%  97.5%
## edges                 -58.50 -20.500 -1.50000 17.5000 54.500
## nodefactor.deg.pers.1 -34.00 -11.000  0.00000 12.0000 35.000
## nodefactor.deg.pers.2 -37.00 -13.000  0.00000 12.0000 36.000
## nodefactor.race..wa.B -22.00  -5.997  2.00320 10.0032 26.003
## nodefactor.race..wa.H -31.98  -8.978  2.02200 14.0220 37.022
## nodefactor.region.EW  -30.56 -10.561  0.43923 11.4392 31.439
## nodefactor.region.OW  -58.13 -19.131 -0.13057 19.8694 56.869
## nodematch.race..wa.B  -11.18  -5.179 -2.17872  0.8213  7.821
## nodematch.race..wa.H  -18.31  -8.312 -2.31243  3.6876 15.688
## nodematch.race..wa.O  -49.89 -14.890  2.11028 20.1103 55.110
## absdiff.sqrt.age      -55.15 -19.472  0.06771 19.1786 56.556
## 
## 
## Sample statistics cross-correlations:
##                           edges nodefactor.deg.pers.1
## edges                 1.0000000            0.39996517
## nodefactor.deg.pers.1 0.3999652            1.00000000
## nodefactor.deg.pers.2 0.4259049            0.05367137
## nodefactor.race..wa.B 0.2538942            0.09265997
## nodefactor.race..wa.H 0.3418588            0.15043543
## nodefactor.region.EW  0.3616464            0.15581205
## nodefactor.region.OW  0.6193141            0.22468449
## nodematch.race..wa.B  0.1037485            0.03301298
## nodematch.race..wa.H  0.1504515            0.07253975
## nodematch.race..wa.O  0.8070925            0.32037481
## absdiff.sqrt.age      0.5431607            0.21858183
##                       nodefactor.deg.pers.2 nodefactor.race..wa.B
## edges                            0.42590485           0.253894181
## nodefactor.deg.pers.1            0.05367137           0.092659966
## nodefactor.deg.pers.2            1.00000000           0.088540630
## nodefactor.race..wa.B            0.08854063           1.000000000
## nodefactor.race..wa.H            0.13815911          -0.004539635
## nodefactor.region.EW             0.15815699           0.040950006
## nodefactor.region.OW             0.24732882           0.126905921
## nodematch.race..wa.B             0.03135354           0.557499165
## nodematch.race..wa.H             0.06160943           0.003861636
## nodematch.race..wa.O             0.35475913          -0.088528804
## absdiff.sqrt.age                 0.22622389           0.133692253
##                       nodefactor.race..wa.H nodefactor.region.EW
## edges                           0.341858791           0.36164644
## nodefactor.deg.pers.1           0.150435431           0.15581205
## nodefactor.deg.pers.2           0.138159111           0.15815699
## nodefactor.race..wa.B          -0.004539635           0.04095001
## nodefactor.race..wa.H           1.000000000           0.23129413
## nodefactor.region.EW            0.231294130           1.00000000
## nodefactor.region.OW            0.192038659           0.05669375
## nodematch.race..wa.B            0.001925968           0.01331005
## nodematch.race..wa.H            0.639998740           0.13071048
## nodematch.race..wa.O           -0.081913216           0.26477614
## absdiff.sqrt.age                0.191518228           0.19118412
##                       nodefactor.region.OW nodematch.race..wa.B
## edges                           0.61931409         0.1037484567
## nodefactor.deg.pers.1           0.22468449         0.0330129763
## nodefactor.deg.pers.2           0.24732882         0.0313535429
## nodefactor.race..wa.B           0.12690592         0.5574991655
## nodefactor.race..wa.H           0.19203866         0.0019259683
## nodefactor.region.EW            0.05669375         0.0133100464
## nodefactor.region.OW            1.00000000         0.0465494856
## nodematch.race..wa.B            0.04654949         1.0000000000
## nodematch.race..wa.H            0.07761130        -0.0001605015
## nodematch.race..wa.O            0.51886481         0.0328519438
## absdiff.sqrt.age                0.33824247         0.0515356640
##                       nodematch.race..wa.H nodematch.race..wa.O
## edges                         0.1504514686           0.80709248
## nodefactor.deg.pers.1         0.0725397454           0.32037481
## nodefactor.deg.pers.2         0.0616094263           0.35475913
## nodefactor.race..wa.B         0.0038616357          -0.08852880
## nodefactor.race..wa.H         0.6399987402          -0.08191322
## nodefactor.region.EW          0.1307104771           0.26477614
## nodefactor.region.OW          0.0776113048           0.51886481
## nodematch.race..wa.B         -0.0001605015           0.03285194
## nodematch.race..wa.H          1.0000000000           0.06440933
## nodematch.race..wa.O          0.0644093334           1.00000000
## absdiff.sqrt.age              0.0845970197           0.43653880
##                       absdiff.sqrt.age
## edges                       0.54316072
## nodefactor.deg.pers.1       0.21858183
## nodefactor.deg.pers.2       0.22622389
## nodefactor.race..wa.B       0.13369225
## nodefactor.race..wa.H       0.19151823
## nodefactor.region.EW        0.19118412
## nodefactor.region.OW        0.33824247
## nodematch.race..wa.B        0.05153566
## nodematch.race..wa.H        0.08459702
## nodematch.race..wa.O        0.43653880
## absdiff.sqrt.age            1.00000000
## 
## Sample statistics auto-correlation:
## Chain 1 
##                  edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0      1.000000000            1.00000000           1.000000000
## Lag 1e+05 -0.005293560            0.04580469          -0.019605892
## Lag 2e+05  0.021285733            0.02001586          -0.008767053
## Lag 3e+05  0.007328327            0.01254249          -0.017467294
## Lag 4e+05  0.014131069           -0.01184943           0.016485409
## Lag 5e+05 -0.015391061           -0.02549088           0.005099705
##           nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0               1.000000000           1.000000000          1.000000000
## Lag 1e+05           0.071261857           0.121462478          0.006008265
## Lag 2e+05           0.017608654           0.075913234          0.032029541
## Lag 3e+05          -0.006253184           0.026245985          0.034350695
## Lag 4e+05           0.006359347           0.015666757         -0.013799286
## Lag 5e+05           0.042492403          -0.002036662          0.009299024
##           nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0              1.000000000           1.00000000          1.000000000
## Lag 1e+05          0.007148178           0.16644005          0.342484122
## Lag 2e+05         -0.003049443           0.04449474          0.145435846
## Lag 3e+05          0.032491506           0.05731573          0.071695262
## Lag 4e+05          0.027910356           0.02988396          0.028621776
## Lag 5e+05          0.031758263           0.01388689         -0.009458021
##           nodematch.race..wa.O absdiff.sqrt.age
## Lag 0              1.000000000      1.000000000
## Lag 1e+05          0.001508030     -0.003105105
## Lag 2e+05         -0.003665394      0.037585708
## Lag 3e+05          0.032103947      0.012358136
## Lag 4e+05          0.009942418     -0.007415915
## Lag 5e+05         -0.022343928     -0.018024290
## Chain 2 
##                  edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0      1.000000000           1.000000000           1.000000000
## Lag 1e+05 -0.005702368           0.026833648           0.023716076
## Lag 2e+05  0.010588181           0.019131377           0.010839306
## Lag 3e+05  0.008285297           0.005830353           0.015981521
## Lag 4e+05  0.014942467           0.010721085          -0.016408090
## Lag 5e+05  0.034404815          -0.002321470           0.006494392
##           nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0               1.000000000            1.00000000           1.00000000
## Lag 1e+05           0.033129751            0.11577337           0.04315336
## Lag 2e+05           0.013714622            0.03359602           0.02074684
## Lag 3e+05          -0.021783203            0.03763816          -0.01377397
## Lag 4e+05          -0.027985825            0.03911414          -0.02030157
## Lag 5e+05          -0.002755909            0.02419932           0.02972016
##           nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0              1.000000000          1.000000000           1.00000000
## Lag 1e+05          0.004819800          0.179193182           0.33604039
## Lag 2e+05         -0.002623491          0.059377139           0.17442967
## Lag 3e+05          0.001734381          0.002997265           0.10382879
## Lag 4e+05          0.016512312          0.003086446           0.07568856
## Lag 5e+05          0.017195248          0.003636133           0.04088698
##           nodematch.race..wa.O absdiff.sqrt.age
## Lag 0             1.0000000000      1.000000000
## Lag 1e+05         0.0343152296      0.013710106
## Lag 2e+05         0.0201490357     -0.001567227
## Lag 3e+05        -0.0007944605     -0.013476206
## Lag 4e+05         0.0190649151      0.017049113
## Lag 5e+05         0.0318063768      0.030502760
## Chain 3 
##                   edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0      1.0000000000           1.000000000           1.000000000
## Lag 1e+05 -0.0023127554           0.026779886           0.029821562
## Lag 2e+05  0.0039928305           0.005646198           0.018438065
## Lag 3e+05 -0.0005696995          -0.013989453          -0.011088865
## Lag 4e+05  0.0027787549          -0.038295688          -0.008601104
## Lag 5e+05  0.0118866469          -0.025121489           0.023101688
##           nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0               1.000000000          1.0000000000          1.000000000
## Lag 1e+05           0.067476853          0.1299366910          0.008497376
## Lag 2e+05          -0.003580596          0.0406808834          0.013688716
## Lag 3e+05          -0.014073147          0.0324685488         -0.009953501
## Lag 4e+05          -0.023600782          0.0009930865         -0.004732366
## Lag 5e+05          -0.040538496          0.0210760051          0.010984170
##           nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0              1.000000000           1.00000000           1.00000000
## Lag 1e+05          0.020645462           0.16971141           0.34371370
## Lag 2e+05         -0.001135087           0.03881282           0.17747363
## Lag 3e+05          0.003518142           0.04238478           0.08088470
## Lag 4e+05          0.019942307          -0.01489772           0.02904910
## Lag 5e+05         -0.014509410          -0.04150348           0.02393723
##           nodematch.race..wa.O absdiff.sqrt.age
## Lag 0             1.0000000000      1.000000000
## Lag 1e+05         0.0001439271      0.003761680
## Lag 2e+05         0.0376418444      0.016824750
## Lag 3e+05        -0.0209856939      0.038637512
## Lag 4e+05         0.0057281231      0.012907778
## Lag 5e+05        -0.0082628028     -0.003680867
## Chain 4 
##                  edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0      1.000000000           1.000000000            1.00000000
## Lag 1e+05  0.031117749           0.026666024            0.03739183
## Lag 2e+05 -0.009970223           0.004621065            0.02405590
## Lag 3e+05 -0.003086740           0.006048212            0.01887761
## Lag 4e+05  0.027628775           0.012042929           -0.01135316
## Lag 5e+05 -0.001126760           0.025499386            0.01375181
##           nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0              1.0000000000           1.000000000           1.00000000
## Lag 1e+05          0.0528394081           0.117818497           0.01306985
## Lag 2e+05          0.0024475845           0.067073848          -0.02039454
## Lag 3e+05          0.0246565873           0.009273808           0.02521637
## Lag 4e+05          0.0004341354          -0.001045780           0.02852425
## Lag 5e+05          0.0038299948           0.009331017           0.00698654
##           nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0              1.000000000           1.00000000           1.00000000
## Lag 1e+05          0.031094977           0.18780861           0.33724675
## Lag 2e+05         -0.002535764           0.03873053           0.18626463
## Lag 3e+05         -0.014456507           0.02227264           0.09723999
## Lag 4e+05          0.028152111           0.02039556           0.05670532
## Lag 5e+05          0.001613010           0.00694822           0.03213768
##           nodematch.race..wa.O absdiff.sqrt.age
## Lag 0              1.000000000      1.000000000
## Lag 1e+05          0.024361104      0.020415028
## Lag 2e+05         -0.011663159      0.017503493
## Lag 3e+05         -0.009622830      0.016431976
## Lag 4e+05          0.029281952      0.026724881
## Lag 5e+05          0.006630604     -0.009558783
## Chain 5 
##                  edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0      1.000000000          1.0000000000            1.00000000
## Lag 1e+05  0.037023763          0.0358414885            0.04168991
## Lag 2e+05  0.020943056          0.0221090322            0.01727855
## Lag 3e+05  0.004763418         -0.0118848498            0.02514273
## Lag 4e+05 -0.005145204          0.0006386401           -0.00381406
## Lag 5e+05 -0.015578149          0.0371103871           -0.01866116
##           nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0               1.000000000            1.00000000          1.000000000
## Lag 1e+05           0.051280612            0.11154106          0.037396705
## Lag 2e+05           0.030330474            0.04607259          0.039368975
## Lag 3e+05           0.003328961            0.01660608          0.026593192
## Lag 4e+05          -0.026609274            0.03931004         -0.016167306
## Lag 5e+05           0.002151416            0.01961920          0.005479975
##           nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0              1.000000000         1.0000000000           1.00000000
## Lag 1e+05          0.018199318         0.1964556219           0.31961733
## Lag 2e+05          0.035458599         0.0664757793           0.13206720
## Lag 3e+05         -0.004136924         0.0055495736           0.06228487
## Lag 4e+05          0.007986066         0.0004050579           0.03755850
## Lag 5e+05          0.011113845        -0.0025452172           0.02044208
##           nodematch.race..wa.O absdiff.sqrt.age
## Lag 0              1.000000000     1.0000000000
## Lag 1e+05          0.030723539     0.0088327715
## Lag 2e+05          0.005316949    -0.0053238251
## Lag 3e+05          0.012522791     0.0001082466
## Lag 4e+05         -0.005274666     0.0178974829
## Lag 5e+05         -0.004820966    -0.0151301138
## Chain 6 
##                  edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0      1.000000000          1.0000000000           1.000000000
## Lag 1e+05  0.008519498          0.0273823711           0.014922439
## Lag 2e+05  0.032607352          0.0067687523           0.017835685
## Lag 3e+05 -0.029688971         -0.0006921469          -0.039210689
## Lag 4e+05 -0.011353922         -0.0139289111           0.006451153
## Lag 5e+05  0.007072129         -0.0321016726          -0.002646642
##           nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0               1.000000000           1.000000000         1.0000000000
## Lag 1e+05           0.052432149           0.125164533         0.0176374257
## Lag 2e+05           0.021352221           0.042979048         0.0014615935
## Lag 3e+05           0.032228459           0.007710214         0.0003346499
## Lag 4e+05          -0.001710624          -0.009701574        -0.0005798581
## Lag 5e+05          -0.027511115           0.012684979        -0.0028409892
##           nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0              1.000000000          1.000000000           1.00000000
## Lag 1e+05          0.028085812          0.157833856           0.36263058
## Lag 2e+05          0.042956515          0.052056930           0.17864497
## Lag 3e+05         -0.005714479          0.033391537           0.08961543
## Lag 4e+05          0.006135763          0.007463110           0.03038678
## Lag 5e+05          0.001245613          0.004149323           0.01466065
##           nodematch.race..wa.O absdiff.sqrt.age
## Lag 0              1.000000000      1.000000000
## Lag 1e+05          0.009989653      0.011278160
## Lag 2e+05          0.024154864      0.020320739
## Lag 3e+05         -0.028116910      0.001744618
## Lag 4e+05         -0.004641422     -0.022476928
## Lag 5e+05         -0.003063877      0.013368985
## Chain 7 
##                  edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0      1.000000000          1.0000000000          1.0000000000
## Lag 1e+05 -0.011333709          0.0004387242          0.0017491655
## Lag 2e+05 -0.007319982         -0.0192388064          0.0098213443
## Lag 3e+05  0.005574105          0.0122862871          0.0006574904
## Lag 4e+05  0.012753676          0.0094488785          0.0049591217
## Lag 5e+05  0.018146661         -0.0057640196         -0.0012615657
##           nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0               1.000000000          1.0000000000          1.000000000
## Lag 1e+05           0.027290469          0.0997687150          0.017815313
## Lag 2e+05           0.031354836          0.0538139311          0.017187151
## Lag 3e+05           0.004188196          0.0468356690          0.004354206
## Lag 4e+05           0.003690554          0.0016733822          0.009582845
## Lag 5e+05          -0.026388073          0.0008791984          0.007423787
##           nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0             1.0000000000          1.000000000          1.000000000
## Lag 1e+05         0.0160186022          0.206548283          0.317172600
## Lag 2e+05        -0.0257450299          0.080444059          0.160488663
## Lag 3e+05        -0.0072518415          0.036660257          0.084265495
## Lag 4e+05         0.0005570283         -0.001741812          0.051147200
## Lag 5e+05         0.0029493583         -0.016522742          0.008056318
##           nodematch.race..wa.O absdiff.sqrt.age
## Lag 0             1.0000000000       1.00000000
## Lag 1e+05        -0.0137762124       0.00761486
## Lag 2e+05        -0.0074517985      -0.01071302
## Lag 3e+05         0.0018169218      -0.01015128
## Lag 4e+05        -0.0008833292       0.01727140
## Lag 5e+05         0.0226659320       0.02023964
## Chain 8 
##                  edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0      1.000000000           1.000000000           1.000000000
## Lag 1e+05 -0.006167298           0.029974348           0.020183847
## Lag 2e+05 -0.005442440          -0.004442949           0.005046315
## Lag 3e+05 -0.002714087          -0.020964491           0.011221175
## Lag 4e+05  0.006183734          -0.008844030          -0.010734423
## Lag 5e+05 -0.031189929          -0.014513607          -0.024812337
##           nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0              1.000000e+00            1.00000000          1.000000000
## Lag 1e+05          3.488321e-02            0.10877384          0.035992777
## Lag 2e+05         -6.016804e-05            0.04843700          0.005331684
## Lag 3e+05          4.774266e-03            0.04154758          0.017031869
## Lag 4e+05          1.394160e-03            0.01167025          0.020608149
## Lag 5e+05          3.301648e-02            0.01110975         -0.022880475
##           nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0              1.000000000          1.000000000          1.000000000
## Lag 1e+05         -0.010105580          0.200560144          0.306601117
## Lag 2e+05          0.006094280          0.080352088          0.149244265
## Lag 3e+05         -0.014094653          0.014152647          0.067178918
## Lag 4e+05         -0.004292082         -0.001001567          0.043592985
## Lag 5e+05         -0.027550571          0.016768333          0.009103252
##           nodematch.race..wa.O absdiff.sqrt.age
## Lag 0              1.000000000     1.0000000000
## Lag 1e+05         -0.001833237     0.0004100609
## Lag 2e+05          0.012516983    -0.0257138714
## Lag 3e+05         -0.003523858     0.0233915355
## Lag 4e+05          0.002086771     0.0114786829
## Lag 5e+05         -0.023818681    -0.0315281184
## 
## Sample statistics burn-in diagnostic (Geweke):
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2 
##                0.6115                1.8548               -0.6848 
## nodefactor.race..wa.B nodefactor.race..wa.H  nodefactor.region.EW 
##                1.1251               -0.9310               -0.3712 
##  nodefactor.region.OW  nodematch.race..wa.B  nodematch.race..wa.H 
##                2.0193                1.5306               -1.5868 
##  nodematch.race..wa.O      absdiff.sqrt.age 
##                0.3057                0.5037 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2 
##            0.54086004            0.06362077            0.49345726 
## nodefactor.race..wa.B nodefactor.race..wa.H  nodefactor.region.EW 
##            0.26053015            0.35185217            0.71049090 
##  nodefactor.region.OW  nodematch.race..wa.B  nodematch.race..wa.H 
##            0.04346055            0.12586506            0.11255927 
##  nodematch.race..wa.O      absdiff.sqrt.age 
##            0.75982324            0.61443789 
## Joint P-value (lower = worse):  0.1753326 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2 
##                0.7223                0.8729               -0.5022 
## nodefactor.race..wa.B nodefactor.race..wa.H  nodefactor.region.EW 
##               -0.2799                2.7447                0.2370 
##  nodefactor.region.OW  nodematch.race..wa.B  nodematch.race..wa.H 
##                0.2398               -0.9160                2.2021 
##  nodematch.race..wa.O      absdiff.sqrt.age 
##               -0.1608                2.1219 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2 
##           0.470098791           0.382718017           0.615545377 
## nodefactor.race..wa.B nodefactor.race..wa.H  nodefactor.region.EW 
##           0.779541178           0.006057346           0.812653436 
##  nodefactor.region.OW  nodematch.race..wa.B  nodematch.race..wa.H 
##           0.810481713           0.359647290           0.027655646 
##  nodematch.race..wa.O      absdiff.sqrt.age 
##           0.872285369           0.033850009 
## Joint P-value (lower = worse):  0.2323312 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2 
##                0.2665               -2.0365               -0.1881 
## nodefactor.race..wa.B nodefactor.race..wa.H  nodefactor.region.EW 
##               -1.0442                0.3118               -0.8963 
##  nodefactor.region.OW  nodematch.race..wa.B  nodematch.race..wa.H 
##                0.6009               -1.3906                0.7861 
##  nodematch.race..wa.O      absdiff.sqrt.age 
##                0.6732                1.2129 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2 
##            0.78981785            0.04169969            0.85076023 
## nodefactor.race..wa.B nodefactor.race..wa.H  nodefactor.region.EW 
##            0.29638638            0.75519185            0.37010586 
##  nodefactor.region.OW  nodematch.race..wa.B  nodematch.race..wa.H 
##            0.54790404            0.16434307            0.43178463 
##  nodematch.race..wa.O      absdiff.sqrt.age 
##            0.50081716            0.22515176 
## Joint P-value (lower = worse):  0.318997 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2 
##               1.01504               0.59876               0.18257 
## nodefactor.race..wa.B nodefactor.race..wa.H  nodefactor.region.EW 
##              -0.03015              -0.07265               0.46190 
##  nodefactor.region.OW  nodematch.race..wa.B  nodematch.race..wa.H 
##               0.85946               0.75310               0.91404 
##  nodematch.race..wa.O      absdiff.sqrt.age 
##               1.74969               0.18362 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2 
##            0.31008513            0.54933562            0.85513576 
## nodefactor.race..wa.B nodefactor.race..wa.H  nodefactor.region.EW 
##            0.97595098            0.94208082            0.64415662 
##  nodefactor.region.OW  nodematch.race..wa.B  nodematch.race..wa.H 
##            0.39008946            0.45139165            0.36069366 
##  nodematch.race..wa.O      absdiff.sqrt.age 
##            0.08017129            0.85431131 
## Joint P-value (lower = worse):  0.8909712 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2 
##              0.009625              1.071616             -0.684502 
## nodefactor.race..wa.B nodefactor.race..wa.H  nodefactor.region.EW 
##              0.608153             -1.843711              0.706592 
##  nodefactor.region.OW  nodematch.race..wa.B  nodematch.race..wa.H 
##              1.696182              1.233685              0.254572 
##  nodematch.race..wa.O      absdiff.sqrt.age 
##              1.406832              0.182161 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2 
##            0.99232029            0.28389248            0.49365833 
## nodefactor.race..wa.B nodefactor.race..wa.H  nodefactor.region.EW 
##            0.54308578            0.06522530            0.47982035 
##  nodefactor.region.OW  nodematch.race..wa.B  nodematch.race..wa.H 
##            0.08985139            0.21732035            0.79905405 
##  nodematch.race..wa.O      absdiff.sqrt.age 
##            0.15947707            0.85545651 
## Joint P-value (lower = worse):  0.04451563 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2 
##                1.4462                0.6401               -0.1260 
## nodefactor.race..wa.B nodefactor.race..wa.H  nodefactor.region.EW 
##               -0.5743                1.2494                0.8125 
##  nodefactor.region.OW  nodematch.race..wa.B  nodematch.race..wa.H 
##                0.6450               -0.6609                1.6447 
##  nodematch.race..wa.O      absdiff.sqrt.age 
##                1.7586                0.2159 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2 
##             0.1481079             0.5220930             0.8997369 
## nodefactor.race..wa.B nodefactor.race..wa.H  nodefactor.region.EW 
##             0.5657949             0.2115336             0.4164942 
##  nodefactor.region.OW  nodematch.race..wa.B  nodematch.race..wa.H 
##             0.5189108             0.5086916             0.1000325 
##  nodematch.race..wa.O      absdiff.sqrt.age 
##             0.0786495             0.8290902 
## Joint P-value (lower = worse):  0.8884442 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2 
##               0.25834              -1.75934               1.58531 
## nodefactor.race..wa.B nodefactor.race..wa.H  nodefactor.region.EW 
##              -1.07857               1.94494               1.27107 
##  nodefactor.region.OW  nodematch.race..wa.B  nodematch.race..wa.H 
##               0.52291              -1.13162               0.06172 
##  nodematch.race..wa.O      absdiff.sqrt.age 
##              -0.80419               1.14834 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2 
##            0.79614656            0.07851962            0.11289640 
## nodefactor.race..wa.B nodefactor.race..wa.H  nodefactor.region.EW 
##            0.28077793            0.05178165            0.20370268 
##  nodefactor.region.OW  nodematch.race..wa.B  nodematch.race..wa.H 
##            0.60103671            0.25779516            0.95078404 
##  nodematch.race..wa.O      absdiff.sqrt.age 
##            0.42128826            0.25082764 
## Joint P-value (lower = worse):  0.1457331 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2 
##              -0.15725              -0.10393              -0.58146 
## nodefactor.race..wa.B nodefactor.race..wa.H  nodefactor.region.EW 
##              -0.15382               0.43965               0.87092 
##  nodefactor.region.OW  nodematch.race..wa.B  nodematch.race..wa.H 
##               0.89326               1.49813               0.34646 
##  nodematch.race..wa.O      absdiff.sqrt.age 
##               0.08083              -1.18821 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2 
##             0.8750439             0.9172246             0.5609334 
## nodefactor.race..wa.B nodefactor.race..wa.H  nodefactor.region.EW 
##             0.8777511             0.6601936             0.3838001 
##  nodefactor.region.OW  nodematch.race..wa.B  nodematch.race..wa.H 
##             0.3717202             0.1340988             0.7290002 
##  nodematch.race..wa.O      absdiff.sqrt.age 
##             0.9355735             0.2347501 
## Joint P-value (lower = worse):  0.499685 .
## Warning in formals(fun): argument is not a function

## 
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).

Model 7

## Sample statistics summary:
## 
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05 
## Number of chains = 8 
## Sample size per chain = 3750 
## 
## 1. Empirical mean and standard deviation for each variable,
##    plus standard error of the mean:
## 
##                          Mean     SD Naive SE Time-series SE
## edges                 -0.9230 28.787  0.16620        0.18593
## nodefactor.deg.pers.1 -0.1031 17.469  0.10086        0.11389
## nodefactor.deg.pers.2  0.0045 18.652  0.10768        0.12771
## nodefactor.race..wa.B  2.4911 12.514  0.07225        0.08535
## nodefactor.race..wa.H  1.4485 17.515  0.10112        0.17080
## nodefactor.region.EW  -0.2812 18.227  0.10523        0.16231
## nodefactor.region.OW   0.1298 33.250  0.19197        0.20777
## nodematch.race..wa.B  -2.0628  4.858  0.02805        0.04394
## nodematch.race..wa.H  -2.3635  8.542  0.04932        0.11964
## nodematch.race..wa.O   3.8377 26.126  0.15084        0.17308
## absdiff.sqrt.age       0.8042 28.515  0.16463        0.18231
## 
## 2. Quantiles for each variable:
## 
##                         2.5%     25%     50%     75%  97.5%
## edges                 -57.50 -20.500 -1.5000 18.5000 55.500
## nodefactor.deg.pers.1 -34.00 -12.000  0.0000 12.0000 34.000
## nodefactor.deg.pers.2 -36.00 -12.000  0.0000 13.0000 37.000
## nodefactor.race..wa.B -22.00  -5.997  2.0032 11.0032 27.003
## nodefactor.race..wa.H -32.98  -9.978  1.0220 13.0220 36.022
## nodefactor.region.EW  -35.56 -13.561  0.4392 12.4392 36.439
## nodefactor.region.OW  -64.13 -22.131 -0.1306 21.8694 65.869
## nodematch.race..wa.B  -11.18  -5.179 -2.1787  0.8213  7.821
## nodematch.race..wa.H  -19.31  -8.312 -2.3124  3.6876 14.688
## nodematch.race..wa.O  -46.89 -13.890  3.1103 21.1103 55.110
## absdiff.sqrt.age      -54.97 -18.351  0.8125 20.0558 56.996
## 
## 
## Sample statistics cross-correlations:
##                           edges nodefactor.deg.pers.1
## edges                 1.0000000            0.39803603
## nodefactor.deg.pers.1 0.3980360            1.00000000
## nodefactor.deg.pers.2 0.4265749            0.05266697
## nodefactor.race..wa.B 0.2754407            0.10618653
## nodefactor.race..wa.H 0.3431995            0.15255616
## nodefactor.region.EW  0.3117805            0.14904874
## nodefactor.region.OW  0.5795201            0.20767434
## nodematch.race..wa.B  0.1058512            0.03274386
## nodematch.race..wa.H  0.1435687            0.06196018
## nodematch.race..wa.O  0.8064535            0.31178496
## absdiff.sqrt.age      0.5408588            0.21681228
##                       nodefactor.deg.pers.2 nodefactor.race..wa.B
## edges                            0.42657491           0.275440744
## nodefactor.deg.pers.1            0.05266697           0.106186532
## nodefactor.deg.pers.2            1.00000000           0.103652379
## nodefactor.race..wa.B            0.10365238           1.000000000
## nodefactor.race..wa.H            0.14819383          -0.009688964
## nodefactor.region.EW             0.13584110           0.026104636
## nodefactor.region.OW             0.22963090           0.123645203
## nodematch.race..wa.B             0.03672171           0.554118408
## nodematch.race..wa.H             0.06453201           0.001400343
## nodematch.race..wa.O             0.34894917          -0.065514553
## absdiff.sqrt.age                 0.22695322           0.155692041
##                       nodefactor.race..wa.H nodefactor.region.EW
## edges                          0.3431994808          0.311780464
## nodefactor.deg.pers.1          0.1525561647          0.149048737
## nodefactor.deg.pers.2          0.1481938251          0.135841096
## nodefactor.race..wa.B         -0.0096889639          0.026104636
## nodefactor.race..wa.H          1.0000000000          0.227869550
## nodefactor.region.EW           0.2278695500          1.000000000
## nodefactor.region.OW           0.1741556626         -0.004739117
## nodematch.race..wa.B           0.0007333622          0.008087375
## nodematch.race..wa.H           0.6342243801          0.127337395
## nodematch.race..wa.O          -0.0800913160          0.221405949
## absdiff.sqrt.age               0.1874269960          0.163926801
##                       nodefactor.region.OW nodematch.race..wa.B
## edges                          0.579520124         0.1058512378
## nodefactor.deg.pers.1          0.207674337         0.0327438606
## nodefactor.deg.pers.2          0.229630900         0.0367217088
## nodefactor.race..wa.B          0.123645203         0.5541184078
## nodefactor.race..wa.H          0.174155663         0.0007333622
## nodefactor.region.EW          -0.004739117         0.0080873751
## nodefactor.region.OW           1.000000000         0.0387071391
## nodematch.race..wa.B           0.038707139         1.0000000000
## nodematch.race..wa.H           0.069194620         0.0010921528
## nodematch.race..wa.O           0.492383179         0.0370213482
## absdiff.sqrt.age               0.311263695         0.0616358277
##                       nodematch.race..wa.H nodematch.race..wa.O
## edges                          0.143568666           0.80645350
## nodefactor.deg.pers.1          0.061960177           0.31178496
## nodefactor.deg.pers.2          0.064532011           0.34894917
## nodefactor.race..wa.B          0.001400343          -0.06551455
## nodefactor.race..wa.H          0.634224380          -0.08009132
## nodefactor.region.EW           0.127337395           0.22140595
## nodefactor.region.OW           0.069194620           0.49238318
## nodematch.race..wa.B           0.001092153           0.03702135
## nodematch.race..wa.H           1.000000000           0.05951206
## nodematch.race..wa.O           0.059512063           1.00000000
## absdiff.sqrt.age               0.079368442           0.43312724
##                       absdiff.sqrt.age
## edges                       0.54085879
## nodefactor.deg.pers.1       0.21681228
## nodefactor.deg.pers.2       0.22695322
## nodefactor.race..wa.B       0.15569204
## nodefactor.race..wa.H       0.18742700
## nodefactor.region.EW        0.16392680
## nodefactor.region.OW        0.31126369
## nodematch.race..wa.B        0.06163583
## nodematch.race..wa.H        0.07936844
## nodematch.race..wa.O        0.43312724
## absdiff.sqrt.age            1.00000000
## 
## Sample statistics auto-correlation:
## Chain 1 
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0      1.00000000            1.00000000            1.00000000
## Lag 1e+05  0.06769847            0.08450526            0.12427567
## Lag 2e+05  0.01913605            0.03598618            0.04909196
## Lag 3e+05 -0.01195674            0.01418958            0.04173636
## Lag 4e+05  0.02833462            0.02836868            0.01153597
## Lag 5e+05 -0.01753768            0.01964117            0.02594178
##           nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0                1.00000000            1.00000000           1.00000000
## Lag 1e+05            0.09004216            0.27349212           0.31137911
## Lag 2e+05            0.02041443            0.16821377           0.16190176
## Lag 3e+05            0.03033915            0.11929116           0.10847211
## Lag 4e+05            0.01434047            0.07685472           0.07813862
## Lag 5e+05            0.04063541            0.04487599           0.06572183
##           nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0              1.000000000           1.00000000            1.0000000
## Lag 1e+05          0.043676801           0.32841516            0.6243771
## Lag 2e+05         -0.011942795           0.19521696            0.4256352
## Lag 3e+05          0.005001991           0.11395529            0.3043325
## Lag 4e+05         -0.007661757           0.06358034            0.2137205
## Lag 5e+05         -0.005959610           0.03986989            0.1623219
##           nodematch.race..wa.O absdiff.sqrt.age
## Lag 0               1.00000000      1.000000000
## Lag 1e+05           0.07277617      0.045824270
## Lag 2e+05           0.01454248      0.006605204
## Lag 3e+05           0.02010015      0.018133145
## Lag 4e+05           0.03555090      0.053995987
## Lag 5e+05          -0.02389293      0.012927706
## Chain 2 
##                  edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0      1.000000000           1.000000000           1.000000000
## Lag 1e+05  0.058127368           0.106784945           0.084638962
## Lag 2e+05  0.015981474           0.030917770           0.053413382
## Lag 3e+05 -0.002345449           0.007706333          -0.005218518
## Lag 4e+05  0.023762489           0.015328473           0.007115044
## Lag 5e+05  0.029154806           0.018762052           0.027315140
##           nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0               1.000000000            1.00000000           1.00000000
## Lag 1e+05           0.104696126            0.26781482           0.32775576
## Lag 2e+05           0.047993514            0.15354901           0.14110339
## Lag 3e+05           0.028429536            0.10833921           0.07518084
## Lag 4e+05           0.018589691            0.06829804           0.03246813
## Lag 5e+05           0.003874657            0.07363898           0.02601542
##           nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0              1.000000000          1.000000000            1.0000000
## Lag 1e+05          0.054702581          0.350565021            0.5913932
## Lag 2e+05         -0.006931929          0.190634917            0.4006833
## Lag 3e+05          0.012351141          0.083244843            0.2963322
## Lag 4e+05          0.004772773          0.033128156            0.2476580
## Lag 5e+05          0.002691049          0.007670699            0.1835250
##           nodematch.race..wa.O absdiff.sqrt.age
## Lag 0              1.000000000      1.000000000
## Lag 1e+05          0.083256837      0.059988661
## Lag 2e+05         -0.001586909      0.045773387
## Lag 3e+05         -0.002775111      0.009448536
## Lag 4e+05          0.015176725      0.034558122
## Lag 5e+05          0.018336616      0.024023083
## Chain 3 
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0      1.00000000            1.00000000            1.00000000
## Lag 1e+05  0.08264806            0.08678509            0.12362922
## Lag 2e+05  0.02418816            0.01216556            0.05112193
## Lag 3e+05 -0.01556547            0.01997431            0.02453039
## Lag 4e+05 -0.02173085           -0.01095952           -0.00673351
## Lag 5e+05  0.03597062            0.02978357            0.01268308
##           nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0                1.00000000            1.00000000           1.00000000
## Lag 1e+05            0.12733748            0.26787709           0.30310559
## Lag 2e+05            0.02496455            0.15303157           0.15061422
## Lag 3e+05            0.01385812            0.09061993           0.06231537
## Lag 4e+05            0.01768839            0.06833543           0.03994750
## Lag 5e+05            0.01608102            0.08663445           0.04287938
##           nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0              1.000000000           1.00000000            1.0000000
## Lag 1e+05          0.072291693           0.36793260            0.5954800
## Lag 2e+05          0.026190735           0.17626935            0.4128870
## Lag 3e+05          0.043426229           0.09710004            0.2913816
## Lag 4e+05          0.003300484           0.05027481            0.2150877
## Lag 5e+05          0.011305925           0.01389337            0.1724547
##           nodematch.race..wa.O absdiff.sqrt.age
## Lag 0             1.0000000000      1.000000000
## Lag 1e+05         0.1038410622      0.067287476
## Lag 2e+05         0.0416023746      0.037905928
## Lag 3e+05        -0.0006556904      0.004060092
## Lag 4e+05        -0.0016237958      0.017588305
## Lag 5e+05         0.0187123507      0.007348827
## Chain 4 
##                  edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0      1.000000000          1.0000000000           1.000000000
## Lag 1e+05  0.082090345          0.0941312974           0.133765547
## Lag 2e+05  0.055422913          0.0273794031           0.058153450
## Lag 3e+05  0.027160862          0.0377146706           0.035485847
## Lag 4e+05 -0.003148203         -0.0003487154           0.005315735
## Lag 5e+05  0.019631761         -0.0297129099           0.009003562
##           nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0               1.000000000            1.00000000           1.00000000
## Lag 1e+05           0.147167292            0.28895239           0.31784171
## Lag 2e+05           0.048974458            0.16996550           0.15139281
## Lag 3e+05           0.011295667            0.12601999           0.10221487
## Lag 4e+05           0.041337600            0.10637520           0.08390794
## Lag 5e+05           0.008612259            0.05051538           0.06906387
##           nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0              1.000000000           1.00000000            1.0000000
## Lag 1e+05          0.064483707           0.33494875            0.6179727
## Lag 2e+05          0.029482801           0.16094630            0.4282253
## Lag 3e+05          0.021882323           0.09478520            0.3309401
## Lag 4e+05         -0.006627717           0.06752424            0.2696910
## Lag 5e+05          0.003625802           0.06924359            0.1980597
##           nodematch.race..wa.O absdiff.sqrt.age
## Lag 0               1.00000000     1.0000000000
## Lag 1e+05           0.09074607     0.0643487620
## Lag 2e+05           0.06067677     0.0511831876
## Lag 3e+05           0.02762856     0.0038282015
## Lag 4e+05          -0.03252625    -0.0009830458
## Lag 5e+05           0.01196113     0.0149702040
## Chain 5 
##                   edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0      1.0000000000            1.00000000            1.00000000
## Lag 1e+05  0.0871839656            0.10640477            0.13304830
## Lag 2e+05 -0.0006012904            0.02460845            0.03242494
## Lag 3e+05 -0.0047012209            0.02806610            0.03697350
## Lag 4e+05  0.0407090025            0.01394590            0.01380634
## Lag 5e+05  0.0273277794            0.03759809            0.04606206
##           nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0              1.0000000000            1.00000000           1.00000000
## Lag 1e+05          0.1144855523            0.28890091           0.33201168
## Lag 2e+05          0.0445736911            0.15591160           0.17544415
## Lag 3e+05          0.0156715456            0.08658692           0.13330484
## Lag 4e+05          0.0062961280            0.08084373           0.09830808
## Lag 5e+05          0.0007647875            0.07842940           0.05864423
##           nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0               1.00000000           1.00000000            1.0000000
## Lag 1e+05           0.07788923           0.35746436            0.6085691
## Lag 2e+05          -0.01504171           0.15225196            0.4096029
## Lag 3e+05          -0.02711553           0.08008288            0.2832178
## Lag 4e+05          -0.02479692           0.03924806            0.1963766
## Lag 5e+05           0.02502108           0.02714032            0.1403657
##           nodematch.race..wa.O absdiff.sqrt.age
## Lag 0               1.00000000      1.000000000
## Lag 1e+05           0.09467206      0.099903560
## Lag 2e+05           0.01517600      0.043591668
## Lag 3e+05           0.01277006      0.020914831
## Lag 4e+05           0.01863300     -0.004383458
## Lag 5e+05           0.02116707      0.006963833
## Chain 6 
##                   edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0      1.0000000000            1.00000000            1.00000000
## Lag 1e+05  0.0632885938            0.08115076            0.09478295
## Lag 2e+05  0.0309815021            0.04876841            0.03836926
## Lag 3e+05  0.0009134427            0.01017291            0.03922493
## Lag 4e+05  0.0048518356            0.01203061            0.01192058
## Lag 5e+05 -0.0010088766           -0.03785248            0.01660604
##           nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0                1.00000000            1.00000000           1.00000000
## Lag 1e+05            0.11564811            0.29078848           0.33022509
## Lag 2e+05            0.05173619            0.17511071           0.13699029
## Lag 3e+05            0.01835578            0.12604080           0.05365017
## Lag 4e+05            0.02072823            0.10906178           0.03836945
## Lag 5e+05           -0.01062245            0.06892829           0.02449346
##           nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0              1.000000000           1.00000000            1.0000000
## Lag 1e+05          0.061240785           0.39054962            0.6105833
## Lag 2e+05          0.010127619           0.19843181            0.4358168
## Lag 3e+05         -0.012862872           0.11361069            0.3278611
## Lag 4e+05          0.001138956           0.05713833            0.2557085
## Lag 5e+05         -0.010358893           0.02982058            0.1992266
##           nodematch.race..wa.O absdiff.sqrt.age
## Lag 0              1.000000000      1.000000000
## Lag 1e+05          0.095956929      0.072165663
## Lag 2e+05          0.048676084      0.033918372
## Lag 3e+05          0.011609342      0.014302304
## Lag 4e+05         -0.004769609      0.008187703
## Lag 5e+05          0.003470246     -0.041222715
## Chain 7 
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0     1.000000000            1.00000000            1.00000000
## Lag 1e+05 0.073368698            0.09420235            0.09640380
## Lag 2e+05 0.020388014            0.03979274            0.05475085
## Lag 3e+05 0.013279007            0.03202856            0.02999588
## Lag 4e+05 0.003362564            0.02370104            0.01786371
## Lag 5e+05 0.027038761            0.02466338            0.01241350
##           nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0                1.00000000            1.00000000           1.00000000
## Lag 1e+05            0.11443950            0.29974279           0.32376437
## Lag 2e+05            0.05645318            0.18153577           0.13795350
## Lag 3e+05            0.03953093            0.12184209           0.08805129
## Lag 4e+05            0.01186373            0.09623942           0.06212355
## Lag 5e+05            0.01357648            0.08475468           0.04495137
##           nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0              1.000000000           1.00000000            1.0000000
## Lag 1e+05          0.062729937           0.35681429            0.6115427
## Lag 2e+05         -0.004569052           0.17422438            0.4326321
## Lag 3e+05         -0.019377796           0.09276404            0.3243880
## Lag 4e+05          0.003242795           0.05519628            0.2553474
## Lag 5e+05          0.014765830           0.01878335            0.2112783
##           nodematch.race..wa.O absdiff.sqrt.age
## Lag 0              1.000000000      1.000000000
## Lag 1e+05          0.103656485      0.034980517
## Lag 2e+05          0.036345485      0.003908467
## Lag 3e+05          0.009943704      0.043105316
## Lag 4e+05          0.006174267      0.021437626
## Lag 5e+05          0.032812194      0.015936807
## Chain 8 
##                  edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0      1.000000000            1.00000000          1.0000000000
## Lag 1e+05  0.072984166            0.11499973          0.1100598561
## Lag 2e+05  0.038447790            0.04217309          0.0533309021
## Lag 3e+05 -0.003444058            0.03120280          0.0253988750
## Lag 4e+05  0.001604086            0.01526900          0.0007102311
## Lag 5e+05 -0.005691717           -0.01586836          0.0213135652
##           nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0               1.000000000            1.00000000           1.00000000
## Lag 1e+05           0.113798471            0.28070988           0.31376554
## Lag 2e+05           0.069099433            0.17884966           0.16641411
## Lag 3e+05           0.010606971            0.12177182           0.08600808
## Lag 4e+05           0.002251525            0.09331093           0.03416815
## Lag 5e+05          -0.017514241            0.09291253           0.02934792
##           nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0             1.0000000000           1.00000000            1.0000000
## Lag 1e+05         0.0448147851           0.36730028            0.6164120
## Lag 2e+05         0.0342667595           0.19328599            0.4367704
## Lag 3e+05         0.0006232978           0.09626245            0.3460960
## Lag 4e+05        -0.0161024904           0.04838563            0.2839143
## Lag 5e+05        -0.0214297053           0.01094436            0.2197718
##           nodematch.race..wa.O absdiff.sqrt.age
## Lag 0              1.000000000     1.0000000000
## Lag 1e+05          0.089061904     0.0601552905
## Lag 2e+05          0.019054614     0.0061859777
## Lag 3e+05         -0.022984289    -0.0003997992
## Lag 4e+05         -0.009096074     0.0227710582
## Lag 5e+05          0.011217787     0.0247159221
## 
## Sample statistics burn-in diagnostic (Geweke):
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2 
##              -0.62341              -0.52021               0.09698 
## nodefactor.race..wa.B nodefactor.race..wa.H  nodefactor.region.EW 
##              -1.58954              -1.50051              -0.62173 
##  nodefactor.region.OW  nodematch.race..wa.B  nodematch.race..wa.H 
##              -1.03380              -1.42856              -2.16557 
##  nodematch.race..wa.O      absdiff.sqrt.age 
##              -0.20740               1.73703 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2 
##            0.53301277            0.60291634            0.92274205 
## nodefactor.race..wa.B nodefactor.race..wa.H  nodefactor.region.EW 
##            0.11193890            0.13348321            0.53411876 
##  nodefactor.region.OW  nodematch.race..wa.B  nodematch.race..wa.H 
##            0.30122896            0.15313022            0.03034436 
##  nodematch.race..wa.O      absdiff.sqrt.age 
##            0.83570072            0.08238180 
## Joint P-value (lower = worse):  0.5568118 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2 
##               1.05801               1.05709               0.09707 
## nodefactor.race..wa.B nodefactor.race..wa.H  nodefactor.region.EW 
##               2.19196               0.02444              -0.37513 
##  nodefactor.region.OW  nodematch.race..wa.B  nodematch.race..wa.H 
##               2.38079               1.57388               1.13458 
##  nodematch.race..wa.O      absdiff.sqrt.age 
##               1.18581               1.34409 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2 
##            0.29005143            0.29046840            0.92267224 
## nodefactor.race..wa.B nodefactor.race..wa.H  nodefactor.region.EW 
##            0.02838208            0.98050031            0.70756107 
##  nodefactor.region.OW  nodematch.race..wa.B  nodematch.race..wa.H 
##            0.01727542            0.11551430            0.25655246 
##  nodematch.race..wa.O      absdiff.sqrt.age 
##            0.23569591            0.17892031 
## Joint P-value (lower = worse):  0.2930557 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2 
##              -0.05403               0.18854              -0.98521 
## nodefactor.race..wa.B nodefactor.race..wa.H  nodefactor.region.EW 
##               0.85673              -0.69373               0.79960 
##  nodefactor.region.OW  nodematch.race..wa.B  nodematch.race..wa.H 
##               1.25782               1.63336              -0.43470 
##  nodematch.race..wa.O      absdiff.sqrt.age 
##               0.36144               0.34161 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2 
##             0.9569084             0.8504515             0.3245214 
## nodefactor.race..wa.B nodefactor.race..wa.H  nodefactor.region.EW 
##             0.3915955             0.4878513             0.4239412 
##  nodefactor.region.OW  nodematch.race..wa.B  nodematch.race..wa.H 
##             0.2084562             0.1023927             0.6637807 
##  nodematch.race..wa.O      absdiff.sqrt.age 
##             0.7177736             0.7326454 
## Joint P-value (lower = worse):  0.6518567 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2 
##              0.008198              1.701945              1.145310 
## nodefactor.race..wa.B nodefactor.race..wa.H  nodefactor.region.EW 
##              1.683424              0.395805             -0.626842 
##  nodefactor.region.OW  nodematch.race..wa.B  nodematch.race..wa.H 
##              0.638576              0.918658              0.461561 
##  nodematch.race..wa.O      absdiff.sqrt.age 
##             -0.524976              0.809372 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2 
##            0.99345936            0.08876567            0.25208087 
## nodefactor.race..wa.B nodefactor.race..wa.H  nodefactor.region.EW 
##            0.09229313            0.69224893            0.53076312 
##  nodefactor.region.OW  nodematch.race..wa.B  nodematch.race..wa.H 
##            0.52309855            0.35827460            0.64439619 
##  nodematch.race..wa.O      absdiff.sqrt.age 
##            0.59960021            0.41830132 
## Joint P-value (lower = worse):  0.3607245 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2 
##               0.55876               0.21653               1.48989 
## nodefactor.race..wa.B nodefactor.race..wa.H  nodefactor.region.EW 
##               0.66411               0.91962               0.06934 
##  nodefactor.region.OW  nodematch.race..wa.B  nodematch.race..wa.H 
##              -0.57068               0.06914               0.51709 
##  nodematch.race..wa.O      absdiff.sqrt.age 
##              -0.19737               0.94259 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2 
##             0.5763279             0.8285739             0.1362540 
## nodefactor.race..wa.B nodefactor.race..wa.H  nodefactor.region.EW 
##             0.5066224             0.3577713             0.9447173 
##  nodefactor.region.OW  nodematch.race..wa.B  nodematch.race..wa.H 
##             0.5682133             0.9448763             0.6050915 
##  nodematch.race..wa.O      absdiff.sqrt.age 
##             0.8435404             0.3458913 
## Joint P-value (lower = worse):  0.8826139 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2 
##               0.73170               2.06544              -0.78405 
## nodefactor.race..wa.B nodefactor.race..wa.H  nodefactor.region.EW 
##              -1.78296              -0.39481              -0.02227 
##  nodefactor.region.OW  nodematch.race..wa.B  nodematch.race..wa.H 
##              -0.39978              -2.39130              -0.38838 
##  nodematch.race..wa.O      absdiff.sqrt.age 
##               1.23568               1.02959 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2 
##            0.46435364            0.03888138            0.43301255 
## nodefactor.race..wa.B nodefactor.race..wa.H  nodefactor.region.EW 
##            0.07459238            0.69298277            0.98222861 
##  nodefactor.region.OW  nodematch.race..wa.B  nodematch.race..wa.H 
##            0.68931900            0.01678899            0.69773191 
##  nodematch.race..wa.O      absdiff.sqrt.age 
##            0.21657611            0.30320322 
## Joint P-value (lower = worse):  0.1482674 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2 
##              -0.58178              -0.33449              -0.57731 
## nodefactor.race..wa.B nodefactor.race..wa.H  nodefactor.region.EW 
##               0.05409               1.53982               0.34516 
##  nodefactor.region.OW  nodematch.race..wa.B  nodematch.race..wa.H 
##              -0.46570               0.30696               2.36796 
##  nodematch.race..wa.O      absdiff.sqrt.age 
##              -0.80842              -0.98066 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2 
##            0.56071766            0.73800858            0.56373086 
## nodefactor.race..wa.B nodefactor.race..wa.H  nodefactor.region.EW 
##            0.95686340            0.12360376            0.72997074 
##  nodefactor.region.OW  nodematch.race..wa.B  nodematch.race..wa.H 
##            0.64143232            0.75887312            0.01788669 
##  nodematch.race..wa.O      absdiff.sqrt.age 
##            0.41884832            0.32675988 
## Joint P-value (lower = worse):  0.7430276 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2 
##               -0.0789                1.4728                0.3105 
## nodefactor.race..wa.B nodefactor.race..wa.H  nodefactor.region.EW 
##                0.8449                1.1911                0.9815 
##  nodefactor.region.OW  nodematch.race..wa.B  nodematch.race..wa.H 
##                1.0658               -0.6463                0.5820 
##  nodematch.race..wa.O      absdiff.sqrt.age 
##               -1.2526                0.5329 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2 
##             0.9371138             0.1408110             0.7562176 
## nodefactor.race..wa.B nodefactor.race..wa.H  nodefactor.region.EW 
##             0.3981887             0.2335971             0.3263528 
##  nodefactor.region.OW  nodematch.race..wa.B  nodematch.race..wa.H 
##             0.2865220             0.5180795             0.5605333 
##  nodematch.race..wa.O      absdiff.sqrt.age 
##             0.2103624             0.5941217 
## Joint P-value (lower = worse):  0.2256065 .
## Warning in formals(fun): argument is not a function

## 
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).

Model 8

## Sample statistics summary:
## 
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05 
## Number of chains = 8 
## Sample size per chain = 3750 
## 
## 1. Empirical mean and standard deviation for each variable,
##    plus standard error of the mean:
## 
##                            Mean     SD Naive SE Time-series SE
## edges                 -2.283200 28.590  0.16507        0.17864
## nodefactor.deg.pers.1 -0.009533 17.471  0.10087        0.10981
## nodefactor.deg.pers.2 -0.433367 18.590  0.10733        0.11924
## nodefactor.race..wa.B  2.471667 12.485  0.07208        0.08189
## nodefactor.race..wa.H  2.035133 17.654  0.10193        0.15996
## nodefactor.region.EW  -0.639000 17.859  0.10311        0.14533
## nodefactor.region.OW  -0.513367 32.504  0.18766        0.19638
## nodematch.race..wa.B  -1.948187  4.896  0.02827        0.04099
## nodematch.race..wa.H  -2.149396  8.679  0.05011        0.10868
## nodematch.race..wa.O   2.239051 26.167  0.15107        0.16374
## absdiff.sqrt.age      -0.261876 28.555  0.16486        0.17830
## nodematch.region      -0.092667 29.148  0.16829        0.19223
## 
## 2. Quantiles for each variable:
## 
##                         2.5%     25%     50%     75%  97.5%
## edges                 -57.52 -21.500 -2.5000 17.5000 53.500
## nodefactor.deg.pers.1 -34.00 -12.000  0.0000 12.0000 34.000
## nodefactor.deg.pers.2 -37.00 -13.000 -1.0000 12.0000 36.000
## nodefactor.race..wa.B -22.00  -5.997  2.0032 11.0032 27.003
## nodefactor.race..wa.H -32.98  -9.978  2.0220 14.0220 37.022
## nodefactor.region.EW  -35.56 -12.561 -0.5608 11.4392 34.439
## nodefactor.region.OW  -64.13 -22.131 -0.1306 21.8694 62.869
## nodematch.race..wa.B  -11.18  -5.179 -2.1787  0.8213  7.821
## nodematch.race..wa.H  -19.31  -8.312 -2.3124  3.6876 14.688
## nodematch.race..wa.O  -49.89 -14.890  2.1103 20.1103 54.110
## absdiff.sqrt.age      -56.45 -19.412 -0.5276 18.8196 56.718
## nodematch.region      -56.45 -19.450 -0.4500 19.5500 56.550
## 
## 
## Sample statistics cross-correlations:
##                           edges nodefactor.deg.pers.1
## edges                 1.0000000            0.39068785
## nodefactor.deg.pers.1 0.3906879            1.00000000
## nodefactor.deg.pers.2 0.4297938            0.04507891
## nodefactor.race..wa.B 0.2662599            0.10191372
## nodefactor.race..wa.H 0.3421319            0.14372709
## nodefactor.region.EW  0.3198379            0.13769265
## nodefactor.region.OW  0.5757142            0.20703655
## nodematch.race..wa.B  0.1105136            0.04093834
## nodematch.race..wa.H  0.1461889            0.06231053
## nodematch.race..wa.O  0.8039229            0.30960722
## absdiff.sqrt.age      0.5402218            0.22112245
## nodematch.region      0.8766448            0.33669935
##                       nodefactor.deg.pers.2 nodefactor.race..wa.B
## edges                            0.42979380           0.266259854
## nodefactor.deg.pers.1            0.04507891           0.101913722
## nodefactor.deg.pers.2            1.00000000           0.093997324
## nodefactor.race..wa.B            0.09399732           1.000000000
## nodefactor.race..wa.H            0.14180711          -0.008514599
## nodefactor.region.EW             0.14322363           0.034576420
## nodefactor.region.OW             0.23490603           0.123937652
## nodematch.race..wa.B             0.03546905           0.558842494
## nodematch.race..wa.H             0.06037293           0.002788320
## nodematch.race..wa.O             0.35574166          -0.074956029
## absdiff.sqrt.age                 0.22776625           0.140927504
## nodematch.region                 0.37699730           0.240554962
##                       nodefactor.race..wa.H nodefactor.region.EW
## edges                           0.342131934          0.319837871
## nodefactor.deg.pers.1           0.143727094          0.137692647
## nodefactor.deg.pers.2           0.141807112          0.143223634
## nodefactor.race..wa.B          -0.008514599          0.034576420
## nodefactor.race..wa.H           1.000000000          0.232531189
## nodefactor.region.EW            0.232531189          1.000000000
## nodefactor.region.OW            0.170814164         -0.002264736
## nodematch.race..wa.B           -0.005692733          0.011413230
## nodematch.race..wa.H            0.636548087          0.125039482
## nodematch.race..wa.O           -0.086732623          0.219689430
## absdiff.sqrt.age                0.179611092          0.176118833
## nodematch.region                0.291164315          0.231297690
##                       nodefactor.region.OW nodematch.race..wa.B
## edges                          0.575714209          0.110513570
## nodefactor.deg.pers.1          0.207036546          0.040938341
## nodefactor.deg.pers.2          0.234906027          0.035469049
## nodefactor.race..wa.B          0.123937652          0.558842494
## nodefactor.race..wa.H          0.170814164         -0.005692733
## nodefactor.region.EW          -0.002264736          0.011413230
## nodefactor.region.OW           1.000000000          0.043685877
## nodematch.race..wa.B           0.043685877          1.000000000
## nodematch.race..wa.H           0.062799201         -0.009718621
## nodematch.race..wa.O           0.483664318          0.041850045
## absdiff.sqrt.age               0.311152200          0.050105838
## nodematch.region               0.492278387          0.104532405
##                       nodematch.race..wa.H nodematch.race..wa.O
## edges                          0.146188867           0.80392288
## nodefactor.deg.pers.1          0.062310527           0.30960722
## nodefactor.deg.pers.2          0.060372928           0.35574166
## nodefactor.race..wa.B          0.002788320          -0.07495603
## nodefactor.race..wa.H          0.636548087          -0.08673262
## nodefactor.region.EW           0.125039482           0.21968943
## nodefactor.region.OW           0.062799201           0.48366432
## nodematch.race..wa.B          -0.009718621           0.04185004
## nodematch.race..wa.H           1.000000000           0.05880039
## nodematch.race..wa.O           0.058800386           1.00000000
## absdiff.sqrt.age               0.078559012           0.43727180
## nodematch.region               0.124779162           0.70757297
##                       absdiff.sqrt.age nodematch.region
## edges                       0.54022178        0.8766448
## nodefactor.deg.pers.1       0.22112245        0.3366994
## nodefactor.deg.pers.2       0.22776625        0.3769973
## nodefactor.race..wa.B       0.14092750        0.2405550
## nodefactor.race..wa.H       0.17961109        0.2911643
## nodefactor.region.EW        0.17611883        0.2312977
## nodefactor.region.OW        0.31115220        0.4922784
## nodematch.race..wa.B        0.05010584        0.1045324
## nodematch.race..wa.H        0.07855901        0.1247792
## nodematch.race..wa.O        0.43727180        0.7075730
## absdiff.sqrt.age            1.00000000        0.4731878
## nodematch.region            0.47318779        1.0000000
## 
## Sample statistics auto-correlation:
## Chain 1 
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0     1.000000000          1.0000000000           1.000000000
## Lag 1e+05 0.043734522          0.0452762862           0.067889708
## Lag 2e+05 0.009702439          0.0274039483           0.002161543
## Lag 3e+05 0.020811467          0.0358556822           0.010570927
## Lag 4e+05 0.011826248          0.0003215591          -0.001225525
## Lag 5e+05 0.032169974         -0.0047673324           0.009447099
##           nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0                1.00000000            1.00000000           1.00000000
## Lag 1e+05            0.10786853            0.20548078           0.25635531
## Lag 2e+05            0.05998099            0.10911683           0.10274977
## Lag 3e+05            0.03162528            0.10176333           0.06459846
## Lag 4e+05            0.02958939            0.06637427           0.03987217
## Lag 5e+05            0.02099115            0.04486566           0.02469043
##           nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0              1.000000000           1.00000000            1.0000000
## Lag 1e+05          0.045228938           0.32882151            0.4862013
## Lag 2e+05          0.025700076           0.16920267            0.3183952
## Lag 3e+05         -0.004237268           0.10489950            0.2256394
## Lag 4e+05         -0.009633616           0.06019234            0.1499446
## Lag 5e+05          0.034262557           0.04763393            0.1136707
##           nodematch.race..wa.O absdiff.sqrt.age nodematch.region
## Lag 0              1.000000000       1.00000000       1.00000000
## Lag 1e+05          0.062026242       0.04058624       0.07295450
## Lag 2e+05          0.017175290       0.02508536       0.03268258
## Lag 3e+05          0.013241663       0.03679810       0.03721407
## Lag 4e+05          0.003769886       0.03386080       0.03840731
## Lag 5e+05          0.003816299       0.03176452       0.01906072
## Chain 2 
##                  edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0      1.000000000           1.000000000           1.000000000
## Lag 1e+05  0.055985620           0.066668778           0.093178224
## Lag 2e+05  0.019940993           0.001110535           0.027243369
## Lag 3e+05  0.002188858           0.011075252           0.021388839
## Lag 4e+05 -0.031013938           0.001654960          -0.039155065
## Lag 5e+05  0.003311521           0.001566750          -0.009455574
##           nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0               1.000000000            1.00000000           1.00000000
## Lag 1e+05           0.062295526            0.18909789           0.22747617
## Lag 2e+05           0.013236770            0.11817673           0.11495528
## Lag 3e+05          -0.022433609            0.07345865           0.07280288
## Lag 4e+05          -0.001079992            0.05581825           0.02803557
## Lag 5e+05           0.001177883            0.04285035           0.01901731
##           nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0             1.0000000000          1.000000000            1.0000000
## Lag 1e+05         0.0696093120          0.282090989            0.4996247
## Lag 2e+05         0.0242642265          0.116680212            0.3365303
## Lag 3e+05        -0.0158895617          0.047421341            0.2575139
## Lag 4e+05        -0.0294692349          0.013206352            0.1737947
## Lag 5e+05         0.0002739821         -0.003914393            0.1388402
##           nodematch.race..wa.O absdiff.sqrt.age nodematch.region
## Lag 0              1.000000000       1.00000000      1.000000000
## Lag 1e+05          0.036197951       0.02764664      0.105499387
## Lag 2e+05          0.032421119       0.01615064      0.028925211
## Lag 3e+05          0.037874279       0.01509322      0.021262537
## Lag 4e+05         -0.031565809      -0.01795900     -0.014236014
## Lag 5e+05          0.001607759       0.03637608      0.007904565
## Chain 3 
##                  edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0      1.000000000           1.000000000           1.000000000
## Lag 1e+05  0.058228430           0.078827038           0.077863670
## Lag 2e+05 -0.008589690           0.050920060           0.011501851
## Lag 3e+05  0.002605912           0.005052671          -0.005577724
## Lag 4e+05  0.021665842           0.031666385           0.026293295
## Lag 5e+05 -0.002079748           0.011558214           0.012621064
##           nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0                1.00000000            1.00000000          1.000000000
## Lag 1e+05            0.11735662            0.19821986          0.228705632
## Lag 2e+05            0.06452024            0.09100213          0.108828299
## Lag 3e+05            0.01366363            0.07496830          0.057578355
## Lag 4e+05            0.02313498            0.08234920          0.005553808
## Lag 5e+05           -0.00858346            0.05740002          0.024352709
##           nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0               1.00000000           1.00000000            1.0000000
## Lag 1e+05           0.06001571           0.32324762            0.4915075
## Lag 2e+05          -0.01253109           0.15624339            0.3038971
## Lag 3e+05          -0.02974309           0.05555642            0.2239145
## Lag 4e+05          -0.02364530           0.03836376            0.1600399
## Lag 5e+05          -0.03069267           0.04043075            0.1205130
##           nodematch.race..wa.O absdiff.sqrt.age nodematch.region
## Lag 0             1.0000000000      1.000000000     1.0000000000
## Lag 1e+05         0.0995370517      0.074071150     0.0833437372
## Lag 2e+05         0.0041057905      0.006608629     0.0037962329
## Lag 3e+05         0.0239513620      0.001243460    -0.0006679481
## Lag 4e+05        -0.0031378816      0.013898432     0.0238994471
## Lag 5e+05        -0.0004786696      0.042654505     0.0016025899
## Chain 4 
##                  edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0      1.000000000           1.000000000           1.000000000
## Lag 1e+05  0.019432027           0.054110468           0.073255934
## Lag 2e+05 -0.014268955           0.009566204          -0.010831854
## Lag 3e+05  0.016314029           0.002208092           0.007331121
## Lag 4e+05  0.038120562           0.040485286           0.002762327
## Lag 5e+05 -0.006369983          -0.009766635           0.013134261
##           nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0                1.00000000            1.00000000           1.00000000
## Lag 1e+05            0.10020586            0.22459916           0.20408318
## Lag 2e+05            0.04083383            0.15712389           0.10181753
## Lag 3e+05            0.03675655            0.09084067           0.04160851
## Lag 4e+05            0.04183225            0.09603325           0.05578569
## Lag 5e+05            0.03023276            0.05372364           0.02968607
##           nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0              1.000000000           1.00000000            1.0000000
## Lag 1e+05          0.046344612           0.29486169            0.5329943
## Lag 2e+05         -0.005421391           0.13624992            0.3625291
## Lag 3e+05          0.001812990           0.08562288            0.2820334
## Lag 4e+05          0.007226342           0.07133442            0.2200660
## Lag 5e+05          0.006668920           0.05446152            0.1877993
##           nodematch.race..wa.O absdiff.sqrt.age nodematch.region
## Lag 0              1.000000000      1.000000000      1.000000000
## Lag 1e+05          0.044222569      0.016756290      0.044720479
## Lag 2e+05          0.003767060      0.007003463     -0.022845562
## Lag 3e+05          0.017920092      0.011367346      0.026650168
## Lag 4e+05          0.006493209      0.007387843      0.027685362
## Lag 5e+05          0.005626490     -0.028752545      0.005301128
## Chain 5 
##                  edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0      1.000000000           1.000000000           1.000000000
## Lag 1e+05  0.067526741           0.059014865           0.080213204
## Lag 2e+05  0.010527153           0.004609703           0.030242338
## Lag 3e+05 -0.003617346           0.001983233           0.007098984
## Lag 4e+05 -0.023568487          -0.026026479           0.007402689
## Lag 5e+05 -0.012587047          -0.022183237          -0.013968802
##           nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0               1.000000000            1.00000000           1.00000000
## Lag 1e+05           0.073494233            0.20886628           0.26145863
## Lag 2e+05           0.041917588            0.11845244           0.14165433
## Lag 3e+05           0.009160498            0.10859512           0.04800362
## Lag 4e+05           0.001321283            0.04450234           0.01334600
## Lag 5e+05          -0.019845657            0.05560386           0.03008237
##           nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0               1.00000000          1.000000000            1.0000000
## Lag 1e+05           0.02032692          0.283060406            0.4659494
## Lag 2e+05           0.01465912          0.128204738            0.2892391
## Lag 3e+05           0.01183201          0.033054459            0.2295557
## Lag 4e+05          -0.01409405         -0.001839996            0.1522515
## Lag 5e+05           0.01316502         -0.004191450            0.1089999
##           nodematch.race..wa.O absdiff.sqrt.age nodematch.region
## Lag 0              1.000000000      1.000000000      1.000000000
## Lag 1e+05          0.084242088      0.051827020      0.068822236
## Lag 2e+05          0.002382625      0.012809847      0.022593102
## Lag 3e+05         -0.014230502      0.002921882      0.007223549
## Lag 4e+05         -0.030499592     -0.012607878      0.003119688
## Lag 5e+05         -0.006722295     -0.010541916     -0.004346334
## Chain 6 
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0      1.00000000          1.000000e+00           1.000000000
## Lag 1e+05  0.07164093          6.971138e-02           0.056196213
## Lag 2e+05  0.01379582          2.247606e-02           0.029364851
## Lag 3e+05 -0.01000389          5.970932e-05           0.006765946
## Lag 4e+05  0.01535555          6.300349e-03           0.024063278
## Lag 5e+05  0.03401446          7.444677e-03           0.017040298
##           nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0               1.000000000            1.00000000           1.00000000
## Lag 1e+05           0.075909585            0.22689197           0.26411651
## Lag 2e+05           0.026524500            0.15073361           0.12398903
## Lag 3e+05           0.014091213            0.11453671           0.07201562
## Lag 4e+05          -0.006682786            0.08404663           0.04966221
## Lag 5e+05          -0.004805165            0.07923275           0.05040471
##           nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0              1.000000000          1.000000000            1.0000000
## Lag 1e+05          0.035795562          0.275621821            0.5313388
## Lag 2e+05          0.002069138          0.095002933            0.3944062
## Lag 3e+05         -0.001277394          0.029932830            0.2865855
## Lag 4e+05         -0.011548711          0.023554784            0.2157031
## Lag 5e+05          0.018214502          0.002002011            0.1841413
##           nodematch.race..wa.O absdiff.sqrt.age nodematch.region
## Lag 0              1.000000000      1.000000000       1.00000000
## Lag 1e+05          0.076163295      0.045016696       0.09029961
## Lag 2e+05          0.008149333     -0.007047222       0.02642242
## Lag 3e+05         -0.003907466     -0.016118821       0.01078445
## Lag 4e+05          0.007971958      0.019446175       0.02840745
## Lag 5e+05          0.009098474     -0.015831898       0.02604994
## Chain 7 
##                edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0     1.00000000           1.000000000            1.00000000
## Lag 1e+05 0.04324999           0.066631990            0.10540752
## Lag 2e+05 0.04215013           0.020049717            0.02698084
## Lag 3e+05 0.00159763          -0.003302934            0.03485740
## Lag 4e+05 0.01554202          -0.013668793            0.02142753
## Lag 5e+05 0.01858660          -0.003893079            0.00842313
##           nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0               1.000000000            1.00000000           1.00000000
## Lag 1e+05           0.116015031            0.21466827           0.18865678
## Lag 2e+05           0.053302518            0.14663227           0.07840543
## Lag 3e+05           0.017589327            0.09727127           0.04781706
## Lag 4e+05          -0.011044547            0.08737686           0.05557408
## Lag 5e+05           0.009518817            0.05679969           0.02743568
##           nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0              1.000000000          1.000000000            1.0000000
## Lag 1e+05          0.071171855          0.336107866            0.5139173
## Lag 2e+05          0.017617578          0.136968271            0.3435826
## Lag 3e+05          0.003853949          0.073786385            0.2556272
## Lag 4e+05          0.009172574          0.028666946            0.1853880
## Lag 5e+05          0.018755882          0.006497589            0.1283876
##           nodematch.race..wa.O absdiff.sqrt.age nodematch.region
## Lag 0               1.00000000      1.000000000      1.000000000
## Lag 1e+05           0.05136208      0.061764996      0.078893234
## Lag 2e+05           0.03374283     -0.006013913      0.047393032
## Lag 3e+05           0.01112571      0.007743583     -0.003668781
## Lag 4e+05           0.01801647     -0.000435902      0.010495603
## Lag 5e+05           0.02241667      0.003929233      0.023428056
## Chain 8 
##                edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0     1.00000000           1.000000000           1.000000000
## Lag 1e+05 0.05506833           0.078675102           0.105192387
## Lag 2e+05 0.02129012           0.004796527           0.059199461
## Lag 3e+05 0.01990780          -0.006348493           0.038403430
## Lag 4e+05 0.02094310          -0.014218825           0.003352746
## Lag 5e+05 0.01742436           0.025941427           0.007701987
##           nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0              1.000000e+00            1.00000000           1.00000000
## Lag 1e+05          6.014802e-02            0.18962305           0.23033144
## Lag 2e+05          2.301030e-02            0.12727099           0.12269369
## Lag 3e+05          7.694215e-04            0.07435599           0.05892154
## Lag 4e+05          4.321569e-03            0.04766478           0.04262414
## Lag 5e+05         -7.926816e-05            0.05549153           0.02248845
##           nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0             1.0000000000           1.00000000            1.0000000
## Lag 1e+05         0.0704687168           0.29997511            0.4974240
## Lag 2e+05         0.0066669637           0.11163044            0.3324487
## Lag 3e+05         0.0071931438           0.05503369            0.2325164
## Lag 4e+05         0.0206193049           0.03108122            0.1651131
## Lag 5e+05         0.0007112943           0.03670977            0.1150501
##           nodematch.race..wa.O absdiff.sqrt.age nodematch.region
## Lag 0              1.000000000      1.000000000       1.00000000
## Lag 1e+05          0.078201434      0.045122009       0.06300044
## Lag 2e+05          0.030764193      0.006219144       0.03494591
## Lag 3e+05          0.033287234      0.038553474       0.03272952
## Lag 4e+05          0.029320375      0.023355929       0.02810092
## Lag 5e+05         -0.003767702      0.032968046       0.01698691
## 
## Sample statistics burn-in diagnostic (Geweke):
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2 
##               1.64123              -0.22659               0.07302 
## nodefactor.race..wa.B nodefactor.race..wa.H  nodefactor.region.EW 
##              -1.13096              -1.74440               0.52571 
##  nodefactor.region.OW  nodematch.race..wa.B  nodematch.race..wa.H 
##               2.36640              -0.68706              -1.59760 
##  nodematch.race..wa.O      absdiff.sqrt.age      nodematch.region 
##               2.78729               1.43856               1.20193 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2 
##           0.100749260           0.820741092           0.941789130 
## nodefactor.race..wa.B nodefactor.race..wa.H  nodefactor.region.EW 
##           0.258073888           0.081090101           0.599086995 
##  nodefactor.region.OW  nodematch.race..wa.B  nodematch.race..wa.H 
##           0.017961877           0.492046358           0.110130973 
##  nodematch.race..wa.O      absdiff.sqrt.age      nodematch.region 
##           0.005315072           0.150274679           0.229389978 
## Joint P-value (lower = worse):  0.1653016 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2 
##               1.63774               0.87000               1.91064 
## nodefactor.race..wa.B nodefactor.race..wa.H  nodefactor.region.EW 
##               0.92089               2.85162               2.12278 
##  nodefactor.region.OW  nodematch.race..wa.B  nodematch.race..wa.H 
##              -0.17383              -0.96972               2.17471 
##  nodematch.race..wa.O      absdiff.sqrt.age      nodematch.region 
##               0.07345              -0.69553               0.78069 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2 
##           0.101476583           0.384298252           0.056050705 
## nodefactor.race..wa.B nodefactor.race..wa.H  nodefactor.region.EW 
##           0.357107157           0.004349664           0.033771898 
##  nodefactor.region.OW  nodematch.race..wa.B  nodematch.race..wa.H 
##           0.861999905           0.332184239           0.029651520 
##  nodematch.race..wa.O      absdiff.sqrt.age      nodematch.region 
##           0.941448961           0.486724925           0.434986712 
## Joint P-value (lower = worse):  0.03242592 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2 
##               -1.8516               -0.6710               -0.3776 
## nodefactor.race..wa.B nodefactor.race..wa.H  nodefactor.region.EW 
##               -1.3919                1.1259                0.1312 
##  nodefactor.region.OW  nodematch.race..wa.B  nodematch.race..wa.H 
##               -0.4248               -2.5128                0.7415 
##  nodematch.race..wa.O      absdiff.sqrt.age      nodematch.region 
##               -2.0959               -0.8712               -1.7582 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2 
##            0.06407880            0.50219475            0.70573282 
## nodefactor.race..wa.B nodefactor.race..wa.H  nodefactor.region.EW 
##            0.16395838            0.26021179            0.89563425 
##  nodefactor.region.OW  nodematch.race..wa.B  nodematch.race..wa.H 
##            0.67098274            0.01197626            0.45839640 
##  nodematch.race..wa.O      absdiff.sqrt.age      nodematch.region 
##            0.03608788            0.38365405            0.07871383 
## Joint P-value (lower = worse):  0.4526964 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2 
##              -0.42796              -0.11949              -0.44679 
## nodefactor.race..wa.B nodefactor.race..wa.H  nodefactor.region.EW 
##              -0.17183               1.13353               0.28689 
##  nodefactor.region.OW  nodematch.race..wa.B  nodematch.race..wa.H 
##              -0.10875              -0.03061               1.03805 
##  nodematch.race..wa.O      absdiff.sqrt.age      nodematch.region 
##              -0.72028              -2.31439              -0.11110 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2 
##            0.66868126            0.90488998            0.65502724 
## nodefactor.race..wa.B nodefactor.race..wa.H  nodefactor.region.EW 
##            0.86357316            0.25699174            0.77419415 
##  nodefactor.region.OW  nodematch.race..wa.B  nodematch.race..wa.H 
##            0.91340139            0.97558138            0.29924477 
##  nodematch.race..wa.O      absdiff.sqrt.age      nodematch.region 
##            0.47135097            0.02064655            0.91153501 
## Joint P-value (lower = worse):  0.7937427 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2 
##              -0.51822               0.43516               1.20599 
## nodefactor.race..wa.B nodefactor.race..wa.H  nodefactor.region.EW 
##              -0.10432              -0.17644               0.03668 
##  nodefactor.region.OW  nodematch.race..wa.B  nodematch.race..wa.H 
##              -0.05064               0.50959               0.01259 
##  nodematch.race..wa.O      absdiff.sqrt.age      nodematch.region 
##              -0.22112               0.43211              -0.97706 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2 
##             0.6043058             0.6634477             0.2278201 
## nodefactor.race..wa.B nodefactor.race..wa.H  nodefactor.region.EW 
##             0.9169169             0.8599490             0.9707388 
##  nodefactor.region.OW  nodematch.race..wa.B  nodematch.race..wa.H 
##             0.9596151             0.6103354             0.9899581 
##  nodematch.race..wa.O      absdiff.sqrt.age      nodematch.region 
##             0.8249954             0.6656637             0.3285398 
## Joint P-value (lower = worse):  0.897573 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2 
##                0.2635                0.8207                0.5189 
## nodefactor.race..wa.B nodefactor.race..wa.H  nodefactor.region.EW 
##               -2.6033               -0.2743               -1.2445 
##  nodefactor.region.OW  nodematch.race..wa.B  nodematch.race..wa.H 
##                0.8345               -0.6667               -0.8389 
##  nodematch.race..wa.O      absdiff.sqrt.age      nodematch.region 
##                1.0125                1.0664               -0.3904 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2 
##           0.792158380           0.411833312           0.603815968 
## nodefactor.race..wa.B nodefactor.race..wa.H  nodefactor.region.EW 
##           0.009234146           0.783874334           0.213321598 
##  nodefactor.region.OW  nodematch.race..wa.B  nodematch.race..wa.H 
##           0.404011720           0.504941747           0.401509634 
##  nodematch.race..wa.O      absdiff.sqrt.age      nodematch.region 
##           0.311297173           0.286241265           0.696206577 
## Joint P-value (lower = worse):  0.3183716 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2 
##               1.39289               0.39859               0.24643 
## nodefactor.race..wa.B nodefactor.race..wa.H  nodefactor.region.EW 
##               1.49187               2.17442               2.02798 
##  nodefactor.region.OW  nodematch.race..wa.B  nodematch.race..wa.H 
##               0.17518               1.02345               1.43567 
##  nodematch.race..wa.O      absdiff.sqrt.age      nodematch.region 
##               0.09123               0.64308               1.92523 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2 
##            0.16365187            0.69019448            0.80534828 
## nodefactor.race..wa.B nodefactor.race..wa.H  nodefactor.region.EW 
##            0.13573441            0.02967395            0.04256230 
##  nodefactor.region.OW  nodematch.race..wa.B  nodematch.race..wa.H 
##            0.86094186            0.30609582            0.15109662 
##  nodematch.race..wa.O      absdiff.sqrt.age      nodematch.region 
##            0.92730587            0.52017397            0.05420090 
## Joint P-value (lower = worse):  0.5400757 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2 
##                0.2831                0.7598                0.8050 
## nodefactor.race..wa.B nodefactor.race..wa.H  nodefactor.region.EW 
##               -0.2230                0.3815               -0.1229 
##  nodefactor.region.OW  nodematch.race..wa.B  nodematch.race..wa.H 
##                2.0450                0.8999                0.3956 
##  nodematch.race..wa.O      absdiff.sqrt.age      nodematch.region 
##                0.4601                0.1513               -0.2105 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2 
##            0.77712856            0.44737349            0.42084400 
## nodefactor.race..wa.B nodefactor.race..wa.H  nodefactor.region.EW 
##            0.82355499            0.70282631            0.90219700 
##  nodefactor.region.OW  nodematch.race..wa.B  nodematch.race..wa.H 
##            0.04085674            0.36816333            0.69237202 
##  nodematch.race..wa.O      absdiff.sqrt.age      nodematch.region 
##            0.64547159            0.87971508            0.83330805 
## Joint P-value (lower = worse):  0.6083809 .
## Warning in formals(fun): argument is not a function

## 
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).

Summary of model fit

Model 1

summary(est.m.buildup.unbal[[1]])
## 
## ==========================
## Summary of model fit
## ==========================
## 
## Formula:   nw ~ edges + degrange(from = 2) + offset(nodematch("role.class", 
##     diff = TRUE, keep = 1:2))
## <environment: 0x55a92f5c7dd8>
## 
## Iterations:  163 out of 400 
## 
## Monte Carlo MLE Results:
##                        Estimate Std. Error MCMC % p-value    
## edges                  -8.74450    0.03428      0  <1e-04 ***
## deg2+                      -Inf    0.00000      0  <1e-04 ***
## nodematch.role.class.I     -Inf    0.00000      0  <1e-04 ***
## nodematch.role.class.R     -Inf    0.00000      0  <1e-04 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run 
##   > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
## 
##  Warning: The following terms have infinite coefficient estimates:
##   deg2+ 
## 
##  The following terms are fixed by offset and are not estimated:
##   nodematch.role.class.I nodematch.role.class.R 
## 
## 
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 153
## Crude Coefficient: 5.023881
## Mortality/Exit Rate: 5.302239e-05
## Adjusted Coefficient: 5.040238

Model 2

summary(est.m.buildup.unbal[[2]])
## 
## ==========================
## Summary of model fit
## ==========================
## 
## Formula:   nw ~ edges + nodefactor("race..wa", base = 3) + degrange(from = 2) + 
##     offset(nodematch("role.class", diff = TRUE, keep = 1:2))
## <environment: 0x55a95202b1d8>
## 
## Iterations:  170 out of 400 
## 
## Monte Carlo MLE Results:
##                        Estimate Std. Error MCMC % p-value    
## edges                  -8.73179    0.03876      0 < 1e-04 ***
## nodefactor.race..wa.B  -0.44891    0.08847      0 < 1e-04 ***
## nodefactor.race..wa.H   0.18424    0.06462      0 0.00435 ** 
## deg2+                      -Inf    0.00000      0 < 1e-04 ***
## nodematch.role.class.I     -Inf    0.00000      0 < 1e-04 ***
## nodematch.role.class.R     -Inf    0.00000      0 < 1e-04 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run 
##   > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
## 
##  Warning: The following terms have infinite coefficient estimates:
##   deg2+ 
## 
##  The following terms are fixed by offset and are not estimated:
##   nodematch.role.class.I nodematch.role.class.R 
## 
## 
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 153
## Crude Coefficient: 5.023881
## Mortality/Exit Rate: 5.302239e-05
## Adjusted Coefficient: 5.040238

Model 3

summary(est.m.buildup.unbal[[3]])
## 
## ==========================
## Summary of model fit
## ==========================
## 
## Formula:   nw ~ edges + nodefactor("race..wa", base = 3) + nodematch("race..wa", 
##     diff = TRUE) + degrange(from = 2) + offset(nodematch("role.class", 
##     diff = TRUE, keep = 1:2))
## <environment: 0x55a96f0f0a08>
## 
## Iterations:  157 out of 400 
## 
## Monte Carlo MLE Results:
##                        Estimate Std. Error MCMC % p-value    
## edges                     46.75         NA     NA      NA    
## nodefactor.race..wa.B    -56.06         NA     NA      NA    
## nodefactor.race..wa.H    -55.71         NA     NA      NA    
## nodematch.race..wa.B      57.54         NA     NA      NA    
## nodematch.race..wa.H      57.65         NA     NA      NA    
## nodematch.race..wa.O     -55.43         NA     NA      NA    
## deg2+                      -Inf       0.00      0  <1e-04 ***
## nodematch.role.class.I     -Inf       0.00      0  <1e-04 ***
## nodematch.role.class.R     -Inf       0.00      0  <1e-04 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run 
##   > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
## 
##  Warning: The following terms have infinite coefficient estimates:
##   deg2+ 
## 
##  The following terms are fixed by offset and are not estimated:
##   nodematch.role.class.I nodematch.role.class.R 
## 
## 
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 153
## Crude Coefficient: 5.023881
## Mortality/Exit Rate: 5.302239e-05
## Adjusted Coefficient: 5.040238

Model 4

summary(est.m.buildup.unbal[[4]])
## 
## ==========================
## Summary of model fit
## ==========================
## 
## Formula:   nw ~ edges + nodefactor("deg.pers") + nodefactor("race..wa", 
##     base = 3) + nodematch("race..wa", diff = TRUE) + degrange(from = 2) + 
##     offset(nodematch("role.class", diff = TRUE, keep = 1:2))
## <environment: 0x55a98c39a028>
## 
## Iterations:  108 out of 400 
## 
## Monte Carlo MLE Results:
##                          Estimate Std. Error MCMC % p-value    
## edges                    44.61121 5502.51143    100  0.9935    
## nodefactor.deg.pers.1    -0.36054    0.06311      0  <1e-04 ***
## nodefactor.deg.pers.2    -0.10996    0.05894      0  0.0621 .  
## nodefactor.race..wa.B   -53.80399 5502.51143    100  0.9922    
## nodefactor.race..wa.H   -53.44810 5502.51143    100  0.9922    
## nodematch.race..wa.B     55.28566 5502.51143    100  0.9920    
## nodematch.race..wa.H     55.39054 5502.51143    100  0.9920    
## nodematch.race..wa.O    -53.17687 5502.51143    100  0.9923    
## deg2+                        -Inf    0.00000      0  <1e-04 ***
## nodematch.role.class.I       -Inf    0.00000      0  <1e-04 ***
## nodematch.role.class.R       -Inf    0.00000      0  <1e-04 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run 
##   > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
## 
##  Warning: The following terms have infinite coefficient estimates:
##   deg2+ 
## 
##  The following terms are fixed by offset and are not estimated:
##   nodematch.role.class.I nodematch.role.class.R 
## 
## 
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 153
## Crude Coefficient: 5.023881
## Mortality/Exit Rate: 5.302239e-05
## Adjusted Coefficient: 5.040238

Model 5

summary(est.m.buildup.unbal[[5]])
## 
## ==========================
## Summary of model fit
## ==========================
## 
## Formula:   nw ~ edges + nodefactor("deg.pers") + nodefactor("race..wa", 
##     base = 3) + nodefactor("region", base = 2) + nodematch("race..wa", 
##     diff = TRUE) + degrange(from = 2) + offset(nodematch("role.class", 
##     diff = TRUE, keep = 1:2))
## <environment: 0x55a9a97a21b8>
## 
## Iterations:  127 out of 400 
## 
## Monte Carlo MLE Results:
##                         Estimate Std. Error MCMC % p-value    
## edges                   79.89172         NA     NA      NA    
## nodefactor.deg.pers.1   -0.36978    0.06327      0 < 1e-04 ***
## nodefactor.deg.pers.2   -0.11815    0.05938      0 0.04663 *  
## nodefactor.race..wa.B  -88.82159         NA     NA      NA    
## nodefactor.race..wa.H  -88.41971         NA     NA      NA    
## nodefactor.region.EW    -0.20881    0.06956      0 0.00268 ** 
## nodefactor.region.OW    -0.39047    0.04525      0 < 1e-04 ***
## nodematch.race..wa.B    90.26454         NA     NA      NA    
## nodematch.race..wa.H    90.36732         NA     NA      NA    
## nodematch.race..wa.O   -88.15427         NA     NA      NA    
## deg2+                       -Inf    0.00000      0 < 1e-04 ***
## nodematch.role.class.I      -Inf    0.00000      0 < 1e-04 ***
## nodematch.role.class.R      -Inf    0.00000      0 < 1e-04 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run 
##   > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
## 
##  Warning: The following terms have infinite coefficient estimates:
##   deg2+ 
## 
##  The following terms are fixed by offset and are not estimated:
##   nodematch.role.class.I nodematch.role.class.R 
## 
## 
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 153
## Crude Coefficient: 5.023881
## Mortality/Exit Rate: 5.302239e-05
## Adjusted Coefficient: 5.040238

Model 6

summary(est.m.buildup.unbal[[6]])
## 
## ==========================
## Summary of model fit
## ==========================
## 
## Formula:   nw ~ edges + nodefactor("deg.pers") + nodefactor("race..wa", 
##     base = 3) + nodefactor("region", base = 2) + nodematch("race..wa", 
##     diff = TRUE) + absdiff("sqrt.age") + degrange(from = 2) + 
##     offset(nodematch("role.class", diff = TRUE, keep = 1:2))
## <environment: 0x55a9c6cdcac8>
## 
## Iterations:  84 out of 400 
## 
## Monte Carlo MLE Results:
##                          Estimate Std. Error MCMC % p-value    
## edges                   9.592e+00  2.211e+04    100 0.99965    
## nodefactor.deg.pers.1  -3.688e-01  6.335e-02      0 < 1e-04 ***
## nodefactor.deg.pers.2  -1.145e-01  5.927e-02      0 0.05330 .  
## nodefactor.race..wa.B  -1.741e+01  2.211e+04    100 0.99937    
## nodefactor.race..wa.H  -1.699e+01  2.211e+04    100 0.99939    
## nodefactor.region.EW   -2.180e-01  7.035e-02      0 0.00195 ** 
## nodefactor.region.OW   -3.949e-01  4.471e-02      0 < 1e-04 ***
## nodematch.race..wa.B    1.884e+01  2.211e+04    100 0.99932    
## nodematch.race..wa.H    1.894e+01  2.211e+04    100 0.99932    
## nodematch.race..wa.O   -1.673e+01  2.211e+04    100 0.99940    
## absdiff.sqrt.age       -1.397e+00  4.174e-02      0 < 1e-04 ***
## deg2+                        -Inf  0.000e+00      0 < 1e-04 ***
## nodematch.role.class.I       -Inf  0.000e+00      0 < 1e-04 ***
## nodematch.role.class.R       -Inf  0.000e+00      0 < 1e-04 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run 
##   > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
## 
##  Warning: The following terms have infinite coefficient estimates:
##   deg2+ 
## 
##  The following terms are fixed by offset and are not estimated:
##   nodematch.role.class.I nodematch.role.class.R 
## 
## 
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 153
## Crude Coefficient: 5.023881
## Mortality/Exit Rate: 5.302239e-05
## Adjusted Coefficient: 5.040238

Model 7

summary(est.m.buildup.unbal[[7]])
## 
## ==========================
## Summary of model fit
## ==========================
## 
## Formula:   nw ~ edges + nodefactor("deg.pers") + nodefactor("race..wa", 
##     base = 3) + nodefactor("region", base = 2) + nodematch("race..wa", 
##     diff = TRUE) + absdiff("sqrt.age") + degrange(from = 2) + 
##     offset(nodematch("role.class", diff = TRUE, keep = 1:2)) + 
##     offset(nodemix("region", base = c(1, 3, 6)))
## <environment: 0x55a9e42760e8>
## 
## Iterations:  91 out of 400 
## 
## Monte Carlo MLE Results:
##                          Estimate Std. Error MCMC % p-value    
## edges                   48.864888         NA     NA      NA    
## nodefactor.deg.pers.1   -0.369889         NA     NA      NA    
## nodefactor.deg.pers.2   -0.116519         NA     NA      NA    
## nodefactor.race..wa.B  -56.200380         NA     NA      NA    
## nodefactor.race..wa.H  -55.763224         NA     NA      NA    
## nodefactor.region.EW     0.684947         NA     NA      NA    
## nodefactor.region.OW    -0.009108         NA     NA      NA    
## nodematch.race..wa.B    57.562403         NA     NA      NA    
## nodematch.race..wa.H    57.646202         NA     NA      NA    
## nodematch.race..wa.O   -55.512942         NA     NA      NA    
## absdiff.sqrt.age        -1.397720         NA     NA      NA    
## deg2+                        -Inf   0.000000      0  <1e-04 ***
## nodematch.role.class.I       -Inf   0.000000      0  <1e-04 ***
## nodematch.role.class.R       -Inf   0.000000      0  <1e-04 ***
## mix.region.EW.KC             -Inf   0.000000      0  <1e-04 ***
## mix.region.EW.OW             -Inf   0.000000      0  <1e-04 ***
## mix.region.KC.OW             -Inf   0.000000      0  <1e-04 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run 
##   > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
## 
##  Warning: The following terms have infinite coefficient estimates:
##   deg2+ 
## 
##  The following terms are fixed by offset and are not estimated:
##   nodematch.role.class.I nodematch.role.class.R mix.region.EW.KC mix.region.EW.OW mix.region.KC.OW 
## 
## 
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 153
## Crude Coefficient: 5.023881
## Mortality/Exit Rate: 5.302239e-05
## Adjusted Coefficient: 5.040238

Model 8

summary(est.m.buildup.unbal[[8]])
## 
## ==========================
## Summary of model fit
## ==========================
## 
## Formula:   nw ~ edges + nodefactor("deg.pers") + nodefactor("race..wa", 
##     base = 3) + nodefactor("region", base = 2) + nodematch("race..wa", 
##     diff = TRUE) + absdiff("sqrt.age") + nodematch("region", 
##     diff = FALSE) + degrange(from = 2) + offset(nodematch("role.class", 
##     diff = TRUE, keep = 1:2))
## <environment: 0x55aa01946cd0>
## 
## Iterations:  121 out of 400 
## 
## Monte Carlo MLE Results:
##                         Estimate Std. Error MCMC % p-value    
## edges                   38.79144         NA     NA      NA    
## nodefactor.deg.pers.1   -0.36957    0.06291      0  <1e-04 ***
## nodefactor.deg.pers.2   -0.11506    0.06027      0  0.0563 .  
## nodefactor.race..wa.B  -48.86702         NA     NA      NA    
## nodefactor.race..wa.H  -48.43273         NA     NA      NA    
## nodefactor.region.EW     0.58155    0.06196      0  <1e-04 ***
## nodefactor.region.OW    -0.04167    0.03891      0  0.2841    
## nodematch.race..wa.B    50.24087         NA     NA      NA    
## nodematch.race..wa.H    50.33170         NA     NA      NA    
## nodematch.race..wa.O   -48.18119         NA     NA      NA    
## absdiff.sqrt.age        -1.39776    0.04163      0  <1e-04 ***
## nodematch.region         2.67294    0.07186      0  <1e-04 ***
## deg2+                       -Inf    0.00000      0  <1e-04 ***
## nodematch.role.class.I      -Inf    0.00000      0  <1e-04 ***
## nodematch.role.class.R      -Inf    0.00000      0  <1e-04 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run 
##   > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
## 
##  Warning: The following terms have infinite coefficient estimates:
##   deg2+ 
## 
##  The following terms are fixed by offset and are not estimated:
##   nodematch.role.class.I nodematch.role.class.R 
## 
## 
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 153
## Crude Coefficient: 5.023881
## Mortality/Exit Rate: 5.302239e-05
## Adjusted Coefficient: 5.040238

Network diagnostics

Model 1

(dx_main1 <- netdx(est.m.buildup.unbal[[1]], nsims = 10, nsteps = 1000, ncores = 4, nwstats.formula = est.m.buildup.unbal[[8]]$formation))
## 
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## 
## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
## 
## Formation Diagnostics
## ----------------------- 
##                        Target Sim Mean Pct Diff Sim SD
## edges                  2240.5 2235.633   -0.002 30.021
## nodefactor.deg.pers.1      NA  563.764       NA 16.918
## nodefactor.deg.pers.2      NA  618.830       NA 18.011
## nodefactor.race..wa.B      NA  271.643       NA 13.242
## nodefactor.race..wa.H      NA  486.702       NA 17.660
## nodefactor.region.EW       NA  448.175       NA 16.769
## nodefactor.region.OW       NA 1471.322       NA 31.104
## nodematch.race..wa.B       NA    8.947       NA  2.716
## nodematch.race..wa.H       NA   27.073       NA  5.267
## nodematch.race..wa.O       NA 1542.219       NA 26.557
## absdiff.sqrt.age           NA 2546.397       NA 48.613
## nodematch.region           NA  994.955       NA 25.909
## deg2+                      NA    0.000       NA  0.000
## nodematch.role.class.I     NA    0.000       NA  0.000
## nodematch.role.class.R     NA    0.000       NA  0.000
## 
## Dissolution Diagnostics
## ----------------------- 
##                 Target Sim Mean Pct Diff  Sim SD
## Edge Duration  153.000  125.736   -0.178 120.208
## Pct Edges Diss   0.007    0.007    0.002   0.002
plot(dx_main1, type="formation")

plot(dx_main1, type="duration")

plot(dx_main1, type="dissolution")

Model 2

(dx_main2 <- netdx(est.m.buildup.unbal[[2]], nsims = 10, nsteps = 1000, ncores = 4, nwstats.formula = est.m.buildup.unbal[[8]]$formation))
## 
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## 
## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
## 
## Formation Diagnostics
## ----------------------- 
##                          Target Sim Mean Pct Diff Sim SD
## edges                  2240.500 2237.017   -0.002 30.959
## nodefactor.deg.pers.1        NA  564.620       NA 18.357
## nodefactor.deg.pers.2        NA  618.896       NA 18.957
## nodefactor.race..wa.B   207.997  207.897    0.000 10.492
## nodefactor.race..wa.H   534.978  534.640   -0.001 17.227
## nodefactor.region.EW         NA  458.497       NA 15.644
## nodefactor.region.OW         NA 1467.086       NA 31.545
## nodematch.race..wa.B         NA    4.949       NA  2.026
## nodematch.race..wa.H         NA   32.543       NA  5.398
## nodematch.race..wa.O         NA 1556.691       NA 26.253
## absdiff.sqrt.age             NA 2546.621       NA 54.374
## nodematch.region             NA  988.700       NA 26.077
## deg2+                        NA    0.000       NA  0.000
## nodematch.role.class.I       NA    0.000       NA  0.000
## nodematch.role.class.R       NA    0.000       NA  0.000
## 
## Dissolution Diagnostics
## ----------------------- 
##                 Target Sim Mean Pct Diff  Sim SD
## Edge Duration  153.000  125.931   -0.177 120.375
## Pct Edges Diss   0.007    0.007   -0.001   0.002
plot(dx_main2, type="formation")

plot(dx_main2, type="duration")

plot(dx_main2, type="dissolution")

Model 3

(dx_main3 <- netdx(est.m.buildup.unbal[[3]], nsims = 10, nsteps = 1000, ncores = 4, nwstats.formula = est.m.buildup.unbal[[8]]$formation))
## 
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## 
## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
## 
## Formation Diagnostics
## ----------------------- 
##                          Target Sim Mean Pct Diff Sim SD
## edges                  2240.500 2230.763   -0.004 27.557
## nodefactor.deg.pers.1        NA  565.561       NA 16.934
## nodefactor.deg.pers.2        NA  619.835       NA 16.832
## nodefactor.race..wa.B   207.997  208.568    0.003 12.809
## nodefactor.race..wa.H   534.978  535.601    0.001 18.723
## nodefactor.region.EW         NA  462.354       NA 14.782
## nodefactor.region.OW         NA 1461.143       NA 25.792
## nodematch.race..wa.B     31.179   28.255   -0.094  4.669
## nodematch.race..wa.H    123.312  118.135   -0.042  7.910
## nodematch.race..wa.O   1638.890 1632.984   -0.004 24.374
## absdiff.sqrt.age             NA 2532.996       NA 43.555
## nodematch.region             NA  982.365       NA 27.138
## deg2+                        NA    0.000       NA  0.000
## nodematch.role.class.I       NA    0.000       NA  0.000
## nodematch.role.class.R       NA    0.000       NA  0.000
## 
## Dissolution Diagnostics
## ----------------------- 
##                 Target Sim Mean Pct Diff  Sim SD
## Edge Duration  153.000  125.393   -0.180 120.285
## Pct Edges Diss   0.007    0.007    0.002   0.002
plot(dx_main3, type="formation")

plot(dx_main3, type="duration")

plot(dx_main3, type="dissolution")

Model 4

(dx_main4 <- netdx(est.m.buildup.unbal[[4]], nsims = 10, nsteps = 1000, ncores = 4, nwstats.formula = est.m.buildup.unbal[[8]]$formation))
## 
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## 
## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
## 
## Formation Diagnostics
## ----------------------- 
##                          Target Sim Mean Pct Diff Sim SD
## edges                  2240.500 2234.234   -0.003 28.221
## nodefactor.deg.pers.1   474.000  469.529   -0.009 16.456
## nodefactor.deg.pers.2   605.000  605.287    0.000 18.661
## nodefactor.race..wa.B   207.997  210.285    0.011 13.110
## nodefactor.race..wa.H   534.978  533.440   -0.003 16.421
## nodefactor.region.EW         NA  459.833       NA 14.904
## nodefactor.region.OW         NA 1463.297       NA 29.730
## nodematch.race..wa.B     31.179   29.109   -0.066  4.856
## nodematch.race..wa.H    123.312  118.511   -0.039  7.845
## nodematch.race..wa.O   1638.890 1638.129    0.000 26.364
## absdiff.sqrt.age             NA 2550.844       NA 51.261
## nodematch.region             NA  985.919       NA 26.061
## deg2+                        NA    0.000       NA  0.000
## nodematch.role.class.I       NA    0.000       NA  0.000
## nodematch.role.class.R       NA    0.000       NA  0.000
## 
## Dissolution Diagnostics
## ----------------------- 
##                 Target Sim Mean Pct Diff  Sim SD
## Edge Duration  153.000  125.501   -0.180 120.059
## Pct Edges Diss   0.007    0.007   -0.001   0.002
plot(dx_main4, type="formation")

plot(dx_main4, type="duration")

plot(dx_main4, type="dissolution")

Model 5

(dx_main5 <- netdx(est.m.buildup.unbal[[5]], nsims = 10, nsteps = 1000, ncores = 4, nwstats.formula = est.m.buildup.unbal[[8]]$formation))
## 
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## 
## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
## 
## Formation Diagnostics
## ----------------------- 
##                          Target Sim Mean Pct Diff Sim SD
## edges                  2240.500 2229.784   -0.005 29.429
## nodefactor.deg.pers.1   474.000  470.846   -0.007 19.375
## nodefactor.deg.pers.2   605.000  607.813    0.005 18.641
## nodefactor.race..wa.B   207.997  210.939    0.014 11.368
## nodefactor.race..wa.H   534.978  528.805   -0.012 17.213
## nodefactor.region.EW    445.561  444.798   -0.002 15.827
## nodefactor.region.OW   1278.131 1272.563   -0.004 29.251
## nodematch.race..wa.B     31.179   29.281   -0.061  4.946
## nodematch.race..wa.H    123.312  117.882   -0.044  9.276
## nodematch.race..wa.O   1638.890 1637.202   -0.001 26.676
## absdiff.sqrt.age             NA 2547.884       NA 43.266
## nodematch.region             NA 1052.901       NA 24.958
## deg2+                        NA    0.000       NA  0.000
## nodematch.role.class.I       NA    0.000       NA  0.000
## nodematch.role.class.R       NA    0.000       NA  0.000
## 
## Dissolution Diagnostics
## ----------------------- 
##                 Target Sim Mean Pct Diff  Sim SD
## Edge Duration  153.000  125.152   -0.182 119.799
## Pct Edges Diss   0.007    0.007    0.004   0.002
plot(dx_main5, type="formation")

plot(dx_main5, type="duration")

plot(dx_main5, type="dissolution")

Model 6

(dx_main6 <- netdx(est.m.buildup.unbal[[6]], nsims = 10, nsteps = 1000, ncores = 4, nwstats.formula = est.m.buildup.unbal[[8]]$formation))
## 
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## 
## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
## 
## Formation Diagnostics
## ----------------------- 
##                          Target Sim Mean Pct Diff Sim SD
## edges                  2240.500 2224.575   -0.007 27.779
## nodefactor.deg.pers.1   474.000  471.736   -0.005 18.258
## nodefactor.deg.pers.2   605.000  601.782   -0.005 17.804
## nodefactor.race..wa.B   207.997  210.446    0.012 12.697
## nodefactor.race..wa.H   534.978  523.001   -0.022 16.782
## nodefactor.region.EW    445.561  441.148   -0.010 17.335
## nodefactor.region.OW   1278.131 1268.319   -0.008 29.627
## nodematch.race..wa.B     31.179   29.160   -0.065  4.314
## nodematch.race..wa.H    123.312  112.825   -0.085  8.279
## nodematch.race..wa.O   1638.890 1633.114   -0.004 26.013
## absdiff.sqrt.age       1206.285 1205.860    0.000 24.810
## nodematch.region             NA 1047.087       NA 26.621
## deg2+                        NA    0.000       NA  0.000
## nodematch.role.class.I       NA    0.000       NA  0.000
## nodematch.role.class.R       NA    0.000       NA  0.000
## 
## Dissolution Diagnostics
## ----------------------- 
##                 Target Sim Mean Pct Diff  Sim SD
## Edge Duration  153.000  125.937   -0.177 120.155
## Pct Edges Diss   0.007    0.007    0.001   0.002
plot(dx_main6, type="formation")

plot(dx_main6, type="duration")

plot(dx_main6, type="dissolution")

Model 7

(dx_main7 <- netdx(est.m.buildup.unbal[[7]], nsims = 10, nsteps = 1000, ncores = 4))
## 
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## 
## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
## 
## Formation Diagnostics
## ----------------------- 
##                          Target Sim Mean Pct Diff Sim SD
## edges                  2240.500 2204.729   -0.016 27.361
## nodefactor.deg.pers.1   474.000  474.778    0.002 18.320
## nodefactor.deg.pers.2   605.000  594.970   -0.017 17.240
## nodefactor.race..wa.B   207.997  205.131   -0.014 11.888
## nodefactor.race..wa.H   534.978  501.902   -0.062 18.880
## nodefactor.region.EW    445.561  407.173   -0.086 18.656
## nodefactor.region.OW   1278.131 1270.100   -0.006 29.894
## nodematch.race..wa.B     31.179   26.880   -0.138  4.535
## nodematch.race..wa.H    123.312   97.033   -0.213  8.697
## nodematch.race..wa.O   1638.890 1621.609   -0.011 25.518
## absdiff.sqrt.age       1206.285 1213.460    0.006 24.093
## deg2+                        NA    0.000       NA  0.000
## nodematch.role.class.I       NA    0.000       NA  0.000
## nodematch.role.class.R       NA    0.000       NA  0.000
## mix.region.EW.KC             NA    0.000       NA  0.000
## mix.region.EW.OW             NA    0.000       NA  0.000
## mix.region.KC.OW             NA    0.000       NA  0.000
## 
## Dissolution Diagnostics
## ----------------------- 
##                 Target Sim Mean Pct Diff  Sim SD
## Edge Duration  153.000  125.994   -0.177 120.834
## Pct Edges Diss   0.007    0.007   -0.003   0.002
plot(dx_main7, type="formation")

plot(dx_main7, type="duration")

plot(dx_main7, type="dissolution")

Model 8

(dx_main8 <- netdx(est.m.buildup.unbal[[7]], nsims = 10, nsteps = 1000, ncores = 4))
## 
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## 
## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
## 
## Formation Diagnostics
## ----------------------- 
##                          Target Sim Mean Pct Diff Sim SD
## edges                  2240.500 2196.336   -0.020 31.584
## nodefactor.deg.pers.1   474.000  472.158   -0.004 18.137
## nodefactor.deg.pers.2   605.000  597.268   -0.013 17.768
## nodefactor.race..wa.B   207.997  205.676   -0.011 11.247
## nodefactor.race..wa.H   534.978  499.571   -0.066 19.671
## nodefactor.region.EW    445.561  408.182   -0.084 19.966
## nodefactor.region.OW   1278.131 1264.950   -0.010 35.971
## nodematch.race..wa.B     31.179   27.002   -0.134  4.583
## nodematch.race..wa.H    123.312   97.848   -0.207  9.864
## nodematch.race..wa.O   1638.890 1615.939   -0.014 25.912
## absdiff.sqrt.age       1206.285 1212.473    0.005 29.059
## deg2+                        NA    0.000       NA  0.000
## nodematch.role.class.I       NA    0.000       NA  0.000
## nodematch.role.class.R       NA    0.000       NA  0.000
## mix.region.EW.KC             NA    0.000       NA  0.000
## mix.region.EW.OW             NA    0.000       NA  0.000
## mix.region.KC.OW             NA    0.000       NA  0.000
## 
## Dissolution Diagnostics
## ----------------------- 
##                 Target Sim Mean Pct Diff  Sim SD
## Edge Duration  153.000  124.803   -0.184 118.955
## Pct Edges Diss   0.007    0.007    0.005   0.002
plot(dx_main8, type="formation")

plot(dx_main8, type="duration")

plot(dx_main8, type="dissolution")